STRING (Satuan Tulisan Riset dan Inovasi Teknologi)
Subjects Area : Computing and Informatics, Industrial Engineering, Electrical Engineering, Mechanical Engineering, Applied Mathematical, Physics (Classical and modern)
Implementasi VIKOR pada Sistem Informasi Akademik SMK Ma'arif 1 Temon
Academic Information System (AIS) has become a vital necessity for educational institutions in digitizing academic data management. SMK Ma'arif 1 Temon is a vocational high school with three expertise programs that does not yet have an integrated AIS. The learning data monitoring process is still done manually using office applications, making it less effective in supporting long-term decision making. This research aims to implement the VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) method in the Academic Information System at SMK Ma'arif 1 Temon as a decision support system for determining outstanding students. The VIKOR method is used to solve multi-criteria problems by ranking alternatives based on three main criteria: academic scores (C1) as a benefit criterion with a weight of 0.6, attendance (C2) as a cost criterion with a weight of 0.2, and extracurricular activities (C3) as a benefit criterion with a weight of 0.2. System development uses the Waterfall method which includes the stages of requirement gathering, analysis, design, coding, and testing and deploy. The results showed that the system was successfully built with functionality running according to needs. Based on testing on 15 student data of class X Pharmaceutical Clinical and Community (FKK), the ranking results of outstanding students were obtained with the lowest VIKOR index (Qi) value, namely Alternatif-1 (Qi=0.000) as first rank, Alternatif-14 (Qi=0.130) as second rank, and Alternatif-5 (Qi=0.144) as third rank. This system is expected to assist the school in monitoring academic data and making more objective decisions.
Copyright (c) 2026 Agus Sidiq Purnomo, Anief Fauzan Rozi, Dina Yulina Heriyani (Author) https://creativecommons.org/licenses/by/4.0
Klasifikasi Tipe Dan Kondisi Kulit Wajah dengan Metode EfficientNet-B0 Menggunakan Grad-CAM sebagai Interpretasi Model
Facial skin type and condition identification is an important step in determining appropriate skincare products. However, many people still face difficulties in recognizing their skin conditions independently, while direct consultation requires significant time and cost. This study aims to develop a classification system for facial skin types and conditions based on a Convolutional Neural Network using the EfficientNet-B0 architecture, as well as to apply Grad-CAM as a model interpretability method. The dataset used consists of 3,500 digital images categorized into seven classes, comprising skin types (acne-prone, oily, dry, and normal) and skin conditions (perioral dermatitis, aging, and vitiligo). The research stages include data collection, data preprocessing, data preparation, model training, model evaluation, and Grad-CAM implementation. The model was trained using a transfer learning approach with the Adam optimizer for 5 epochs. Initial testing with a learning rate of 0.0001 resulted in an accuracy of 89%. The system performance then improved significantly, achieving a final accuracy of 98% after adjusting the learning rate to 0.001. Grad-CAM visualization shows that the model focuses on facial regions relevant to each class. Therefore, the proposed method is capable of delivering high classification performance along with transparent visual interpretation.
Copyright (c) 2026 Intan Putri Mansyur Pratama, Nada Firda Khofifah, Anggraini Puspita Sari (Author) https://creativecommons.org/licenses/by/4.0
Klasifikasi Tingkat Stres Berbasis Heart Rate Variability Menggunakan Logistic Regression pada ESP32
Measurement of stress levels relying solely on subjective assessment is often prone to perception bias, thus necessitating an objective method based on physiological biomarkers such as Heart Rate Variability (HRV). This study aims to develop a portable stress detection system using the MAX30102 PPG sensor and ESP32 microcontroller, as well as to analyze changes in HRV parameters before and after a cognitive stimulus. The method used was a pre-post experimental design on 20 subjects, with a mental arithmetic task as the stress trigger. PPG signal data were processed to extract time-domain features (RMSSD, SDNN, and Mean BPM), which were then classified using the Logistic Regression algorithm. Stress level label validation was conducted using the DASS-21 and PSS-10 questionnaires. The results showed a significant physiological response post-stimulus, characterized by an increase in the average Mean BPM from 74 BPM to 84 BPM, and a decrease in heart rate variability values (RMSSD and SDNN), indicating sympathetic nerve activation. The Logistic Regression classification model successfully differentiated stress conditions with an accuracy rate reaching 100% on the test data. However, this perfect achievement on a small-scale dataset (N=20) indicates the need for further testing with a larger sample size in order to minimize the risk of overfitting bias.
Copyright (c) 2026 Aldrey Diriyah, Muhammad Aulia Bahtiar, Suci Fitria, Rudi Setiawan, Nova Resfita (Author) https://creativecommons.org/licenses/by/4.0
Penerapan Metode Content Based Filtering pada Rekomendasi Menu di UMKM Me Love
The culinary industry faces challenges in helping customers find menu items that meet their specific needs on digital platforms, as existing search features tend to be generic. Me Love, a spicy noodle shop offering a diverse range of options regarding flavor preferences and price points, currently lacks a mechanism for automatically recommending products to customers. This study developed a web-based menu recommendation system using the Content-Based Filtering (CBF) method and the Sørensen–Dice Coefficient algorithm to analyze product similarities based on attributes such as category, dish type, and spiciness level. The dataset comprised 50 active menu items. Functional testing via Black-box testing demonstrated that all key features of the admin dashboard and the recommendation filters operated as designed. Meanwhile, performance testing using a confusion matrix yielded a precision of 84% and an accuracy of 94.8%, indicating the system's ability to provide recommendations aligned with the product characteristics sought by users. This system is expected to enhance product search efficiency and the overall user experience within Me Love's digital catalog.
Copyright (c) 2026 Prayoga Adhi Pamungkas, Dwi Hartanti, Aprilisa Arum Sari (Author) https://creativecommons.org/licenses/by/4.0
Integrasi Metode Rula dan Nordic Body Map Untuk Evaluasi Risiko Ergonomi pada Industri Tempe Skala Rumah Tangga
The household tempeh industry is still dominated by manual methods and simple facilities, which trigger ergonomic risks and symptoms of Musculoskeletal Disorders (MSDs). This study, conducted at two tempeh MSMEs in Semarang, identifies risk levels and worker complaints to formulate work improvement proposals using a descriptive quantitative approach. Data were collected through observation, documentation, interviews, body angle measurements, and the Nordic Body Map (NBM) questionnaire, while body posture risk was assessed using the Rapid Upper Limb Assessment (RULA) method.The results show that the majority of production activities, such as washing, boiling, fermenting, and packaging, fall within moderate to very high risk levels with RULA scores ranging from 4 to 7. Dominant musculoskeletal complaints were reported in the neck, shoulders, back, waist, arms, and legs, with the "mildly painful" category reaching 48.21%. These findings demonstrate a significant correlation between non-ergonomic working postures and the physical complaints experienced by workers.
Copyright (c) 2026 Anisya Ulfia, Jazuli (Author) https://creativecommons.org/licenses/by/4.0
Sistem Informasi Kolaborasi Penulis dan Editor dengan Pendekatan Business Process Reengineering
The collaboration process between authors and editors in publishing educational books at PT Tiga Serangkai Pustaka Mandiri is still carried out through email and instant messaging applications. This study aims to design a collaborative information system that supports manuscript management in a more effective and integrated manner. Data were collected through literature study, observation, and interviews, while system development applied the Rapid Application Development (RAD) method. The Business Process Reengineering (BPR) approach was used to redesign workflows to become more efficient. The results show that the proposed system provides centralized manuscript submission, automatic notifications, real-time status monitoring, and role-based access management. Testing using Black Box Testing indicated that all system functions operated according to requirements.
Copyright (c) 2026 Laila Kaltsum Hafizhah Salma, Eko Purwanto, Tri Djoko Santosa (Author) https://creativecommons.org/licenses/by/4.0
Penerapan Metode Content-Based Filtering Pada Rekomendasi Produk di Meubel Pratama Mulya
The furniture industry faces challenges in helping customers find products that meet their needs on digital platforms because existing search features are still general. Meubel Pratama Mulya, a furniture store with a diverse collection of materials and design styles, does not yet have a mechanism to automatically suggest products to customers. This study developed a web-based furniture product recommendation system using the Content-Based Filtering (CBF) method to analyze product similarities based on material and design style attributes. The dataset used consisted of 40 furniture products. The system was proven to be able to generate relevant recommendations based on the highest similarity value between the desired product and the available catalog. Algorithm performance testing showed an average Precision value of 85% and Accuracy of 91%, indicating that the system is able to provide recommendations based on the characteristics of the product sought by users. This system is equipped with an admin dashboard with real-time data trend visualization. With this system, product search efficiency and user experience in the Meubel Pratama Mulya digital catalog are expected to improve.
Copyright (c) 2026 Danu Paradikma, Dwi Hartanti, Vihi Atina (Author) https://creativecommons.org/licenses/by/4.0
Sistem Informasi Monitoring Dan Evaluasi Magang Menggunakan Metode OOAD di PT Tiga Serangkai Pustaka Mandiri
The monitoring and evaluation of internship activities require well-organized data management to ensure that participants’ activities can be effectively monitored and assessed. However, manual internship management often results in several issues, including unsystematic data recording, difficulties in tracking participant activities, and the lack of an integrated evaluation process. This study was conducted to design and develop an internship monitoring and evaluation information system at PT Tiga Serangkai Pustaka Mandiri by applying the Object Oriented Analysis and Design (OOAD) method. The system was developed as a web-based application using the PHP programming language with the Laravel framework, while system modeling was carried out using UML. The system provides three user access levels, namely admin, mentor, and participant, to support the management of participant data, mentor data, activity logbooks, feedback, and evaluation processes. The findings indicate that the developed system can support integrated recording, monitoring, and assessment of internship activities. Therefore, the system contributes to a more structured and systematic management of internship activities.
Copyright (c) 2026 Dias Dwi Utami, Hanifah Permatasari, Joni Maulindar (Author) https://creativecommons.org/licenses/by/4.0
Sistem Informasi Monitoring Anak Tidak Sekolah Berbasis Web Menggunakan Metode Waterfall
The Surakarta City Education Office has an important responsibility in handling the issue of out-of-school children (ATS). However, the data collection and reporting processes are still carried out manually, which leads to several issues, including data entry inaccuracies, processing delays, and less effective monitoring and decision-making. This study focuses on developing a web-based monitoring information system for out-of-school children to support more optimal data management. The system was developed using the waterfall method, which includes requirements analysis, UML-based system design, implementation using Laravel with PHP and MySQL, system testing through Black Box Testing, and system maintenance. The results indicate that the developed system is able to integrate data management and reporting into a structured workflow. Furthermore, all system features operate properly and meet user requirements.
Copyright (c) 2026 Keisha Safa Kamila, Eko Purwanto, Rudi Susanto (Author) https://creativecommons.org/licenses/by/4.0
Analisis Sentimen Google Maps Review Wulan Rent Car Menggunakan Support Vector Machine (SVM)
Google Maps reviews serve as a vital data source for companies including car rental businesses to understand consumer perceptions. Manually evaluating such vast amounts of data is inefficient; therefore, technology-based approaches are required to analyze the emotions or opinions embedded in the text (sentiment analysis). This study aims to classify the sentiment of Google Maps reviews for Wulan Rent Car using the Support Vector Machine (SVM) method. Out of 443 total reviews, 301 contained text and underwent further processing, including labeling via the Bing Liu Opinion Lexicon, data pre-processing, and TF-IDF-based scoring. The labeling results identified 269 positive, 18 neutral, and 14 negative reviews. Model evaluation yielded an accuracy of 84.75%. Based on precision, recall, and F1-score metrics, the model demonstrated strong performance for the positive sentiment class but struggled to optimally classify negative sentiment due to imbalanced data distribution. The findings indicate that while the SVM method applies to sentiment analysis of Wulan Rent Car’s Google Maps reviews, model performance remains influenced by data distribution characteristics, particularly regarding minority classes. Consequently, future research should incorporate data balancing techniques—such as the Synthetic Minority Over-Sampling Technique (SMOTE) or similar methods—to enhance the model's ability to classify all sentiment categories effectively.
Copyright (c) 2026 Septian Wulandari, Dian Novita, Agus Wilson (Author) https://creativecommons.org/licenses/by/4.0
Penerapan Algoritma K-Nearest Neighbor untuk Klasifikasi Persediaan Kain Sasirangan pada UMKM
An inventory classification system plays an important role in supporting stock management among Sasirangan fabric MSMEs in Banjarmasin, South Kalimantan. This study aims to develop a web-based Sasirangan fabric inventory classification system using the K-Nearest Neighbor (KNN) algorithm. The system classifies fabric inventory into three categories: low, medium, and high. This study used 198 inventory data records from MSMEs in Banjarmasin, consisting of 158 training data and 40 testing data. The research stages included data collection, preprocessing, splitting the data into training and testing sets, implementing the KNN algorithm, and evaluating model performance using a confusion matrix and classification evaluation metrics. The evaluation results showed that the KNN model achieved accuracy, precision, recall, and F1-score values of 0.9500 based on the weighted average. Based on the macro average, the KNN model achieved precision, recall, and F1-score values of 0.9484. These results indicate that KNN can classify Sasirangan fabric inventory accurately, stably, and consistently. In addition, KNN demonstrated better performance than Naive Bayes, which achieved an accuracy value of 0.8000. Therefore, improving the quantity and quality of data, conducting feature selection, and optimizing the value of k through parameter tuning are necessary to improve the accuracy of Sasirangan fabric inventory classification.
Copyright (c) 2026 Sry Dhina Pohan, Khairul Huda, Muhammad Fadhli Dzil Ikram (Author) https://creativecommons.org/licenses/by/4.0
Rancang Bangun Sistem Informasi sebagai Strategi Ketahanan Ekonomi Kreatif Kesenian Tanjidor
Tanjidor art is a Betawi cultural heritage with historical and economic value, but it faces challenges in its preservation and economic sustainability due to limitations in promotion, documentation, and the use of digital technology. This research aims to develop digital infrastructure as a strategy for the creative economy resilience of Tanjidor art, with Putra Mayangsari Cijantung as the preservation partner. The research method used is descriptive qualitative, with system development using the Waterfall method, which includes needs analysis, system design, implementation, testing, and maintenance. The developed system is a web-based platform that functions as a medium for promotion, documentation, and information management for Tanjidor artists. The results of this research are a website or digital platform that can expand promotional reach, increase the economic competitiveness of artists, strengthen the resilience of the creative economy, and support the sustainable preservation of Tanjidor art through the use of digital technology.
Copyright (c) 2026 Tri Putri Lestari, Atalya Kurnia Sari, Sutrisno (Author) https://creativecommons.org/licenses/by/4.0
Analisis Kinerja Karyawan dengan Metode Regresi Linear Berganda di PT SINT
Amid the increasingly intense competition in the construction industry, human resource management has become a crucial factor in improving company efficiency and productivity. This study was conducted at PT SINT Production Unit with the aim of examining the effect of work environment, compensation, and motivation on employee performance. The focus of the study is directed at performance-related issues such as tardiness, absence during morning assemblies, and suboptimal achievement of work targets. Using a quantitative approach, this study applies multiple linear regression analysis to examine the influence of each independent variable, both partially and simultaneously, on employee performance. Data were collected through questionnaires distributed to 32 operational employees. The results of the analysis indicate that all independent variables simultaneously have a significant effect on employee performance. Individually, motivation has the greatest contribution, followed by compensation and work environment. These findings provide practical recommendations for management, including the need to improve work motivation, evaluate compensation systems, and enhance working conditions. The study concludes that synergy in managing internal factors can serve as an effective strategy to improve employee performance and company competitiveness. The results also show that work environment, compensation, and motivation significantly affect employee performance with a significance value of < 0.05, with motivation being the most dominant variable.
Copyright (c) 2026 Ahmad Windu Munawar, Anis Sintiya Sari (Author) https://creativecommons.org/licenses/by/4.0
Perancangan Framework Vibe Coding yang Bertanggung Jawab dalam Pengembangan Perangkat Lunak
The advancement of large language models (LLM) has given rise to vibe coding, a prompt-based programming paradigm that enhances productivity but introduces serious risks including reduced code comprehension, low maintainability, accumulation of technical debt, and application security vulnerabilities. This research aims to design a Responsible Vibe Coding Framework that integrates software engineering principles (SOLID), computational thinking, human-AI collaboration, and secure, maintainable software development practices. Employing a qualitative-conceptual approach through systematic literature review of publications from 2015-2025, the study identifies the phenomenon, analyzes supporting theories, and synthesizes a framework comprising five core components: Input Layer (intent specification), AI Assistance Layer (LLM code generation), Human Oversight Layer (human verification), Engineering Guardrails Layer (automated testing and security scanning), and Output Layer (production-ready code). The framework ensures human-centered decision-making, integrated software engineering discipline, and transparency with accountability, comprehensively addressing vibe coding challenges while maintaining code quality, security, and comprehension in educational and industrial contexts.
Copyright (c) 2026 Eko Harli, Ahmad Fauzi, Tria Hadi Kusmanto (Author) https://creativecommons.org/licenses/by/4.0
Implementasi Simple Moving Average dan Rank Order Centroid–Weighted Moving Average untuk Peramalan Produksi Keripik Tempe UMKM X
This study aims to forecast the production demand of tempeh chips at MSME X in Kubu Raya Regency, West Kalimantan. Forecasting was conducted using the Simple Moving Average (SMA) and Weighted Moving Average (WMA) methods with data periods (n) of 3 and 6 months. In the WMA method, weights were determined using the Rank Order Centroid (ROC) approach. The analyzed data consisted of tempeh chip production demand from December 2023 to November 2024, and the forecasting calculations were performed using Microsoft Excel. The accuracy of the forecasting results was evaluated using the Mean Absolute Deviation (MAD) metric. The results showed that the lowest MAD value was obtained using the WMA method with n = 6 (8.91), followed by SMA with n = 6 (10.94), SMA with n = 3 (17.78), and WMA with n = 3 (18.37). These findings indicate that the longer the historical data period used, the better the forecasting accuracy. Moreover, the application of ROC weighting in the WMA method enables predictions to be more responsive to actual trend changes. Based on these results, MSME X is recommended to adopt the WMA method with n = 6 for forecasting tempeh chip production demand.
Copyright (c) 2026 Sariati, Borneo Satria Pratama, Veni Sepriana, Asti Ariati, Febrian Harijal, Ristanto Dwi Nurcahyo, Muhammad Dwi Yoga R., Palentino Desember (Author) https://creativecommons.org/licenses/by/4.0
Perbandingan Logistic Regression dan Random Forest untuk Prediksi Risiko Stroke
Stroke is a leading cause of global mortality that requires early detection through artificial intelligence technologies. This study aims to conduct a comparative analysis between Logistic Regression and Random Forest models in predicting stroke risk based on health and lifestyle data. The study utilized the Stroke Prediction Dataset, which underwent preprocessing, standardization, and class imbalance handling. Model performance was evaluated using Accuracy, Precision, Recall, and ROC-AUC metrics. The results demonstrate that Random Forest significantly outperformed Logistic Regression with an AUC value of 0.990 compared to 0.892. Random Forest proved to be more effective for medical screening, achieving a Recall of 96.29%, which is substantially higher than Logistic Regression at 83.71%. Furthermore, Random Forest demonstrated superior capability in capturing non-linear data patterns in complex variables such as BMI. It is concluded that Random Forest is the recommended method for stroke early detection systems due to its ability to minimize undetected positive cases (false negatives).
Copyright (c) 2026 Aldrey Diriyah, Affan Alfarabi, Fadhlurrohman Arif Mukhlis, Putri Nuriya Salsabila, I Gde Eka Dirgayussa, Nurul Maulidiyah (Author) https://creativecommons.org/licenses/by/4.0
Pengaruh Kejenuhan Proyek Subtype Terhadap Kinerja Pegawai Magang: Studi Kasus di PT X
PT X is company develops a food search application, with one of its main initiatives being the Subtype Project. The project aims to categorize thousands of merchants based on the type of food they offer to enhance the accuracy of the search feature. In its execution, interns in the Operations Department play a vital role but often experience burnout due to the repetitive nature of their tasks. This study aims to examine the relationship between job burnout, age, and length of employment on intern performance using multiple linear regression analysis. A quantitative approach was employed to assess the associations among variables statistically. The results indicate that job burnout (X1) has a significant influence on performance (Y), as shown by its significance value of <!-- [if gte msEquation 12]><m:oMath><m:r>0.000 < 0.05</m:r></m:oMath><![endif]--> <span style="font-size: 12.0pt; line-height: 115%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: 'Times New Roman'; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 2.5pt; mso-text-raise: -2.5pt; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"> . In contrast, age (X2) with a significance value of <!-- [if gte msEquation 12]><m:oMath><m:r>0.374 > 0.05</m:r></m:oMath><![endif]--> <span style="font-size: 12.0pt; line-height: 115%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: 'Times New Roman'; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 2.5pt; mso-text-raise: -2.5pt; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"> , and length of employment (X3) with a significance value of <!-- [if gte msEquation 12]><m:oMath><m:r>0.557 > 0.05</m:r></m:oMath><![endif]--> <span style="font-size: 12.0pt; line-height: 115%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: 'Times New Roman'; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 2.5pt; mso-text-raise: -2.5pt; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"> , are not found to have a significant effect on performance (Y). The study concludes that monotonous tasks can reduce work effectiveness, suggesting that the company should design appropriate human resource management strategies to reduce burnout and maintain productivity, particularly in repetitive projects such as the Subtype Project.
Copyright (c) 2026 Ghema Rivana, Risma Fitriani (Author) https://creativecommons.org/licenses/by/4.0
Rancang Bangun Sistem Informasi Reservasi Ruang Kegiatan Mahasiswa dengan Pemodelan UML
The development of information technology encourages the utilization of web-based systems to improve efficiency in managing information and services. One of its applications is in the reservation system for student activity spaces, which aims to facilitate the booking process and the management of space usage schedules. Previously, the reservation process was carried out manually, making it difficult for users to obtain information about schedule availability and increasing the risk of recording errors. Therefore, this study aims to design and develop a web-based information system for reserving student activity spaces. The system development method used in this study is the System Development Life Cycle (SDLC), which includes the stages of planning, requirements analysis, system design, implementation, and testing. The system design was modeled using Unified Modeling Language (UML), while system testing was conducted using the blackbox testing method. The results show that the developed system is able to provide information on space schedule availability, facilitate the reservation process, and manage booking history in a structured manner, thereby making the reservation process more effective and organized.
Copyright (c) 2026 Chika Putri Pratama, Apriade Voutama (Author) https://creativecommons.org/licenses/by/4.0
Inovasi Alat Pakan Otomatis Berbasis IoT Untuk Monitoring dengan Metode Evaluasi Akurasi RMSE
The development of IoT based automatic chicken feeding device supports efficiency and productivity for poultry farmers. This system is designed using an ESP32 micro-controller integrated with a DHT22 sensor to detect temperature and humidity. It is controlled via a Telegram application and a website, enabling real-time monitoring of data and system status. The research employed a field experimental method by observing the performance of the device in both automatic and manual modes. Additionally, sensor readings were compared with a thermohygrometer to calculate the system’s measurement error. The results show that the system effectively monitors temperature, humidity, and feeding schedules, with a Root Mean Square Error (RMSE) of 1.09°C for temperature and 2.27% RH for humidity. The reliability of the system was analyzed using Standard Deviation (SD), resulting in a temperature SD of 1.61°C and a humidity SD of 4.31% RH. According to the DHT22 data sheet, the tolerance range for accuracy is ±0.5°C for temperature and 2–5% RH for humidity. This automation system successfully performs real-time monitoring, supports both automatic and manual control, and sends notifications via the Telegram platform, with an average response delay of 2.5 seconds. The study demonstrates that the implementation of IoT in the poultry farming sector can significantly improve the efficiency and effectiveness of digital feed management systems.
Copyright (c) 2026 Alberta Yudea Hani Santoso, Maun Budiyanto (Author) https://creativecommons.org/licenses/by/4.0
Analisis Pola Pembelian Pelanggan Menggunakan Algoritma Apriori untuk Strategi Promosi
This research was conducted at a cafe located in South Tangerang City. Although the cafe is quite popular, its promotional strategy management is still carried out conventionally and has not optimally utilized the available transaction data. This results in the determination of promotional packages often missing the target and failing to reflect customer preferences accurately. This research applies data mining techniques using the Apriori algorithm to extract hidden purchasing patterns from transaction data. The research utilized Altair AI Studio software with a large-scale dataset consisting of 27,251 transactions from the period of January to May 2025. The research integrated a pre-processing stage in Microsoft Excel with automatic binary transformation to obtain more precise association rules. The analysis process, using a minimum support of 1% and a minimum confidence of 10%, successfully identified significant purchasing patterns. The main findings reveal a very strong association between the purchase of Nasi Goreng Seafood and Air Mineral, with the highest lift ratio of 2.698 and a confidence value of 35.7%. In addition, patterns of association were found between snack menus (French Fries) and certain coffee variants that showed a positive correlation (lift > 1).
Copyright (c) 2026 zulfikar reza pahlevi, Wiwin Winarti, S.Si., M.Kom. (Author) https://creativecommons.org/licenses/by/4.0
Kendali Paparan UVB Berbasis Arduino untuk Aplikasi Laboratorium
Ultraviolet type B (UVB) radiation has been widely utilized in laboratory research applications. This research develops an Arduino Nano-based UVB radiation control system using 310 nm LED as the radiation source. The system is designed to provide precise and controlled exposure dosage through a user interface with a 16x2 LCD display and push buttons. Electronic circuits include LED driver, power control relay, notification buzzer, and integrated power supply in a portable casing. The device can adjust the dosage in mili Joule units with inter-LED spacing of 5 cm and irradiance of 0.2 mW/cm². Functionality testing shows that the system successfully controls exposure time according to the desired dosage with accuracy of 96-97%. This research results in a portable and efficient UVB chamber tool that is easy to use and can be applied in laboratories
Copyright (c) 2026 Muhamad Nurus Syaifullah (Author) https://creativecommons.org/licenses/by/4.0
Model Prediksi Harga Bitcoin Menggunakan Algoritma SBI-GRU dan SBI-LSTM
The main issue in this study is the high volatility of bitcoin prices. Based on our observations, we found that bitcoin price volatility ranges from 35% to 85%, making bitcoin investments quite risky. Therefore, the purpose of this study is to create a bitcoin price prediction model that can follow this volatility pattern. We created a neural network (NN)-based Bitcoin price prediction model using the Stacked-Bidirectional Gated Recurrent Unit (SBI-GRU) and Stacked-Bidirectional Long Short-Term Memory (SBI-LSTM) algorithms. Both algorithms are suitable and have been proven capable of predicting Bitcoin prices. Furthermore, we also used hyperparameter tuning to improve the accuracy of this prediction model. The tuning parameters used were epoch, batch size, and optimizer. Our research results show that this prediction model is capable of predicting bitcoin prices and their volatility patterns. The benchmarks are as follows: the SBI-GRU model has a MAPE value of 0.0254 and a correlation coefficient of 0.9975. Meanwhile, the SBI-LSTM model has a MAPE value of 0.0333 and a correlation coefficient of 0.9963. The interpretation of these results is that both prediction models, using the SBI-GRU and SBI-LSTM methods, are able to predict bitcoin prices very well. A MAPE value of less than 0.05 makes the prediction model accurate, while a correlation coefficient of 0.99 means that the model is able to follow the volatility pattern.
Copyright (c) 2026 Aryajaya Alamsyah (Author) https://creativecommons.org/licenses/by/4.0
Evaluasi Kinerja File Carving Berbasis NIST pada Media Flashdisk Menggunakan Autopsy, Recuva, dan PhotoRec
This study aims to analyze the performance of file carving software in digital forensic investigations using the National Institute of Standards and Technology (NIST) methodology. The NIST framework is applied due to its systematic and well-structured stages, namely Collection, Examination, Analysis, and Reporting, which support reliable digital forensic processes. A flash drive was used as digital evidence and examined using three file carving tools: Autopsy, Recuva, and PhotoRec. Two testing scenarios were conducted, including file deletion and quick format. The evaluation parameters consisted of processing speed, the number of recovered files, and file integrity based on MD5 hash value matching.The results show that in the deletion scenario, all software tools successfully recovered the test files with 100% accuracy, while Recuva demonstrated the fastest recovery time. In the quick format scenario, Autopsy was unable to perform file carving, whereas Recuva and PhotoRec successfully recovered data. PhotoRec outperformed Recuva in terms of processing speed and recovery accuracy based on MD5 hash verification. These findings indicate that the effectiveness of file carving tools depends on the case scenario rather than a single superior tool. Therefore, selecting an appropriate digital forensic tool is essential to ensure accurate and effective file carving investigations.
Copyright (c) 2026 Rizky Dwitama, Angelo Risang Kadisi, Renita Tri, Sefti Nabila, Susanto (Author) https://creativecommons.org/licenses/by/4.0
Analisis Pemulihan File Terhapus Permanen Menggunakan Disk Drill pada Media Penyimpanan USB
Data loss due to human error or malware attacks is a serious obstacle in the use of digital storage media. The main problem is the assumption that permanent deletion completely erases the data, even though the data is still physically retained on the storage media because the operating system only deletes the metadata pointer. This study applies a digital forensic approach through the Deep Scan feature in Disk Drill software, which integrates metadata scan and file carving techniques. The dataset used in this test is a 32 GB USB Flashdisk containing eight different file format variants (such as DOCX, PDF, and JPG) that have been permanently deleted. The test results show a 100% recovery success rate, where all test files were successfully found and restored in an intact condition (integrity maintained). This study contributes by empirical validation of the reliability of the file carving technique in recognizing file signatures, so that it can be a practical reference in digital forensic investigation procedures and self-service data recovery.
Copyright (c) 2026 Azmi Maulana Firdaus, Nanda Dwi Kurniawan, Fausta Rizky Abriansah, Daffa Nurin Nabil Ma’arif, Susanto (Author) https://creativecommons.org/licenses/by/4.0
Perencanaan Kapasitas Produksi untuk Meningkatkan Efisiensi Produk Peter Kigumi di PT XYZ
The research was conducted at PT XYZ with the aim of improving production efficiency on Peter Kigumi's garment line through capacity planning. The main problem found was a gap between production targets and actual capacity, which caused bottlenecks at several workstations such as hand rib panel sewing, neck overlays, neck stitches, and waistband stitches. The research method uses a time study to measure cycle time, Seven Waste analysis to identify activities that are not value-added, and Fishbone Diagram to find out the root cause of inefficiency. The results of the study show that the main causes of inefficiency are suboptimal operator skills, the absence of standard work procedures, and the lack of utilization of auxiliary equipment. Improvements were made with a Kaizen approach in the form of operator training, technical assistance, and the application of jigs as work aids. Comparison of conditions before and after the repair shows an increase in production capacity quantitatively, where the capacity at the bottleneck station has increased, such as the process of sewing hand rib panels from 97 units/hour to close to the target of 126 units/hour, neck overhead from 94 units/hour, neck stitch from 112 units/hour, and waistband stitch from 84 units/hour which previously had the largest difference, So that there is a significant reduction in capacity gaps at almost all bottleneck stations. The integration of time study and Kaizen methods has proven to be effective in increasing efficiency and productivity in a sustainable manner in the garment industry.
Copyright (c) 2026 Wili Mulyana, Sukanta (Author) https://creativecommons.org/licenses/by/4.0
Transformasi Pemasaran UMKM Bakpia Melalui Analisis Data Mining
The micro, small, and medium enterprises (MSMEs) sector is critical to Indonesia's economy. Especially the food and beverage industry is one of the sectors that is relied on and has rapid growth. Although, business competition is getting tighter for MSMEs today, there needs to be a marketing strategy to improve business. This study aims to optimize marketing strategies in food and beverage MSMEs using data mining method integration, with a case study of MSME Bakpia Tugu. To optimize business improvement, this study integrates data mining methods: Analytical Hierarchy Process (AHP), Association Rules-Market Basket Analysis (AR-MBA), Clustering, and Classification. Data collection in this study used a questionnaire method from 150 respondents for AR-MBA data mining, clustering, and classification, and an expert interview method was used for AHP analysis. The research results get marketing strategy priorities from the results of the AHP analysis, namely through digital marketing strategies, identifying consumer purchasing patterns based on the relationship between items purchased from the results of AR-MBA, grouping customers based on characteristic proximity (Clustering) producing three customer segments that can be optimized for relevant promotional approaches, and Classification to predict customer arrival pattern behaviour. This research can be implemented to improve the food and beverage MSME business in an increasingly competitive market.
Copyright (c) 2025 Yanti Bima Wati, Andi Nurulyunisa Permata Sari Pettalolo, Dwi Adi Purnama (Author) https://creativecommons.org/licenses/by/4.0
Sistem Pendukung Keputusan Penentuan Penerima Beasiswa di SMA Uswatun Hasanah dengan Metode SAW
This study aims to design a Decision Support System (DSS) for determining scholarship recipients at SMA Uswatun Hasanah using the Simple Additive Weighting (SAW) method. The scholarship selection process at the school has previously been conducted manually, which is time-consuming and prone to subjectivity. The proposed system is desktop-based with integrated database support, enabling objective and transparent data processing. The selection criteria include average report card scores, non-academic achievements, extracurricular participation, number of dependents, and parents’ income. The implementation results indicate that the SAW method can accurately generate the ranking of scholarship candidates, thereby improving efficiency, reducing errors, and enhancing fairness in decision-making. This research is expected to serve as a model for implementing DSS in other schools facing similar challenges.
Copyright (c) 2025 Ahmad Fauzi, Khoirunnisa, Lia Susanti (Author) https://creativecommons.org/licenses/by/4.0
Analisis Kesiapan Tim dalam Mengadopsi Software Craftsmanship: Studi Kasus Pengembangan Aplikasi Rapor Online
This study assesses the readiness of a small-scale development team to implement software craftsmanship in the development of an online report card application for early childhood education, utilizing the Extreme Programming (XP) method. This approach was chosen to overcome the quality limitations that often arise in agile-based development. The development process was carried out through two XP iterations with eleven (11) user stories. The results of the study indicate that only some practices, such as standard coding and test-driven development in some functions, can be implemented. Meanwhile, pair programming has not been running optimally. The main finding of this study is that team readiness is a key factor in the successful implementation of software craftsmanship. The contribution of this study lies in the development of an evaluation framework for team readiness in adopting software craftsmanship, especially in the context of software development by small teams in an educational environment.
Copyright (c) 2025 Ahmadi, Arie Surachman, Alpi Mahisha Nugraha (Author) https://creativecommons.org/licenses/by/4.0
Peningkatan Akurasi Klasifikasi Digit melalui Modifikasi CNN dengan Batch Normalization, Dropout, dan Data Augmentation
Digit image classification is a fundamental task in pattern recognition and computer vision. This study aims to improve the performance of a baseline Convolutional Neural Network (CNN) by applying several architectural modifications, including data augmentation, batch normalization, dropout, and deeper convolutional layers. The digit dataset was divided into training and validation sets with an 80:20 ratio. Augmentation techniques such as random rotation, translation, and zoom were applied to enhance data variability and reduce overfitting. Batch normalization was used to stabilize training, while dropout served as a regularization method to prevent overfitting in dense layers. Experimental results demonstrate that the modified CNN achieved high validation accuracy and produced a confusion matrix indicating consistent classification across all digit classes. These findings suggest that enhancing a simple CNN architecture can significantly improve performance without requiring complex transfer learning approaches. This method is suitable for small to medium-scale image classification tasks.
Copyright (c) 2026 Aldrey Diriyah, Khalida Zia Qinthara, Ajeng Windi Setianingsih, Yemima Perangin-Angin, I Gde Eka Dirgayussa (Author) https://creativecommons.org/licenses/by/4.0
Analisis Performa Penjualan Elektronik Menggunakan Metode RFM dan Neural Network (Studi Kasus PT. Makmur Sumber Redjeki)
The development of information technology and increasing business competition require companies to effectively analyze historical sales data to understand customer behavior and improve marketing strategies. This study aims to analyze the electronic sales performance of PT. Makmur Sumber Redjeki by combining the Recency, Frequency, Monetary (RFM) method and a Neural Network model. The RFM method is used to group customers based on the last transaction time, purchase frequency, and transaction value, while the Neural Network model is used to more accurately classify customers into loyal, potential, and at-risk churn categories. The research data consists of customer sales transactions over a one-year period, processed using a supervised learning approach. The analysis stages include data normalization, customer segmentation using RFM, and training a Neural Network model using RFM features as input. The results of the study show that the integration of the RFM method with Risk 38.5%, Loyal 30.8% and Potential 30.8% and Neural Network is able to increase the accuracy of 75% in identifying strategic customer groups and producing useful insights in supporting data-based marketing decision making.
Copyright (c) 2026 Khanes Setiyo Aji, Dr. Winarni, S.T., M.T.I., Dr. Achmad Hindasyah, S.Si., M.Si (Author) https://creativecommons.org/licenses/by/4.0