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Journal of Computer System and Informatics (JoSYC)
Published by Forum Kerjasama Pendidikan TInggi
ISSN : 27147150     EISSN : 27148912     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering
Journal of Computer System and Informatics (JoSYC) covers the whole spectrum of Artificial Inteligent, Computer System, Informatics Technique which includes, but is not limited to: Soft Computing, Distributed Intelligent Systems, Database Management and Information Retrieval, Evolutionary computation and DNA/cellular/molecular computing, Fault detection, Green and Renewable Energy Systems, Human Interface, Human-Computer Interaction, Human Information Processing Hybrid and Distributed Algorithms, High Performance Computing, Information storage, Security, integrity, privacy and trust, Image and Speech Signal Processing, Knowledge Based Systems, Knowledge Networks, Multimedia and Applications, Networked Control Systems, Natural Language Processing Pattern Classification, Speech recognition and synthesis, Robotic Intelligence, Robustness Analysis, Social Intelligence, Ubiquitous, Grid and high performance computing, Virtual Reality in Engineering Applications Web and mobile Intelligence, Big Data
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 450 Documents
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Analisis Ketidakseimbangan Tegangan Baterai dengan Pendekatan Random Forest, K Nearest Neighbors untuk Prediksi Balancing Charger Novianto, Irwan; Hernawan, Septian Rico
Journal of Computer System and Informatics (JoSYC) Vol 6 No 4 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i4.8116

Abstract

Inter-cell voltage imbalance degrades efficiency, accelerates aging, and increases failure risk in electrochemical energy storage systems. This study models and predicts balancing-charger conditions using two machine-learning algorithms Random Forest (RF) and K-Nearest Neighbors (KNN) across packs of 4, 8, 10, and 15 cells with five dataset scales (1,000; 5,000; 10,000; 15,000; and 20,000 samples). Voltage data were obtained through simulation and laboratory measurements on lithium-ion cells within 3.2–4.2 V, then normalized and split into training and testing sets. Performance was evaluated using accuracy, confusion matrices, and feature-importance analysis. Results show RF achieves 0.98 accuracy for 4-cell packs and remains high at 0.93 for 15-cell packs, whereas KNN attains only 0.94 and 0.37 on the same configurations. RF exhibits predictions concentrated along the confusion-matrix diagonal with well-distributed feature weights, indicating robustness to increasing dimensionality. The contributions are threefold: (1) an evaluation framework for comparing classifiers in multi-cell scenarios; (2) empirical evidence of RF’s scalability for detecting balancing conditions from single-voltage inputs; and (3) practical implications for BMS operation more accurate balancing decisions, prioritization of problematic cells, reduced futile equalization cycles, and potential energy savings together with extended service life. These findings recommend RF as a core algorithm for machine-learning-based balancing chargers, particularly for real-world deployment on power-constrained edge devices.
Implementation of Random Forest Optimized with Ant Colony Optimization (ACO) for Breast Cancer Prediction Ht. Barat, Ade Ismiaty Ramadhona; Siregar, Sandy Putra; Poningsih, Poningsih; Windarto, Agus Perdana; Solikhun, Solikhun; Sembiring, Rahmat Widia
Journal of Computer System and Informatics (JoSYC) Vol 6 No 4 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i4.7116

Abstract

Breast cancer is a significant disease impacting women globally, highlighting the necessity for precise and dependable diagnostic models. This study aims to improve breast cancer prediction by optimizing the Random Forest algorithm using Ant Colony Optimization (ACO). This study uses datasets containing various cell characteristics to build and evaluate models. The ACO algorithm is applied to fine-tune the hyperparameters of the Random Forest model and improve its predictive performance. The experimental results showed that the optimized Random Forest model outperformed the baseline model in all evaluation metrics. The optimized model achieved an accuracy of 94.74%, precision of 97.92%, recall 90.38%, an F1 score of 92.93%, and an AUC score of 0, 9449 compared to the basic Random Forest model, with lower scores across all metrics. This improvement highlights the effectiveness of ACOs in improving model performance, especially in reducing false negatives, which are critical for medical diagnosis. This study demonstrates that ACO successfully fine-tunes Random Forest hyperparameters, achieving superior accuracy compared to baseline and outperforming previous optimization methods such as PSO. These findings confirm that the combination of Random Forest and ACO offers a powerful and effective approach to improving the accuracy of breast cancer predictions, making them a valuable tool for clinical decision-making.
Prediksi Insomnia Berdasarkan Aktivitas Pengguna Twitter Menggunakan Natural Language Processing dan Machine Learning Trisna, Trisna; Heliana, Asti
Journal of Computer System and Informatics (JoSYC) Vol 6 No 4 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i4.8081

Abstract

Insomnia is a sleep disorder that is widely experienced by the public and has a significant impact on physical and mental health, as well as productivity. However, early detection of insomnia remains a challenge because its symptoms are difficult to identify directly. This study uses historical data of 13,950 tweets from 4,286 Twitter accounts (January 1–April 30, 2025) to predict potential insomnia using Natural Language Processing (NLP) and machine learning methods. Insomnia labels are determined through an expert-verified keyword-based approach, followed by preprocessing, temporal analysis, and sentiment analysis. Two classification models are used: Support Vector Machine (SVM), which excels at separating classes in high-dimensional data, and Long Short-Term Memory (LSTM), which excels at capturing sequential patterns and temporal context. Preliminary results showed that SVM had 89% accuracy and was superior in the non-insomnia class (precision 0.80, recall 0.97) but suboptimal in insomnia (precision 0.92, recall 0.82), while LSTM had 90% accuracy and was better in insomnia (precision 0.98, recall 0.86) but slightly inferior in non-insomnia (precision 0.81, recall 0.96). Since each model had different strengths, they were combined with a probabilistic ensemble averaging method which resulted in 92% accuracy with balanced improvements in both classes (non-insomnia: precision 0.82, recall 0.99; insomnia: precision 1.00, recall 0.88), making it more reliable than a single model in detecting potential insomnia.
Penentuan Kelayakan Penerima Bantuan Program Keluarga Harapan Menggunakan Algoritma Support Vector Machine Audina, Nys. Sinta; Aviani, Tri Hasanah Bimastari; Daulay, Nelly Khairani
Journal of Computer System and Informatics (JoSYC) Vol 7 No 1 (2025): November 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i4.8064

Abstract

Technological developments, particularly in the field of machine learning, have had a significant impact on supporting data-driven decision making. One of the challenges faced in implementing PKH in Tiang Pumpung Kepungut Subdistrict, Musi Rawas Regency, is [A1] the process of determining aid recipients, which is still done manually. Data is still manually recorded into Excel based on data obtained during the population census. This often causes errors and mistakes during the aid distribution process. To overcome this problem, this study proposes the use of the Support Vector Machine algorithm in the PKH beneficiary classification process. Support Vector Machine is an effective classification method for handling complex and non-linear data with a high degree of accuracy. This study aims to develop a Support Vector Machine-based system to improve efficiency, accuracy, and transparency in the selection process for determining the eligibility of aid recipients. A total of 250 PKH data sets were successfully obtained. The data obtained or collected included several variables, namely name, age, number of family members, occupation, income, number of dependent children, and status. The data was then divided into two sets: 80% for training and 20% for testing from a total of 250 data points. After cleaning the data, the number of data points became 244, with 195 for training and 49 for testing. The results of this study showed that 39 families were eligible to receive assistance and 10 families were not eligible. It is hoped that the resulting system can serve as an innovative solution to support more targeted social assistance distribution in the region.
Model Hybrid dalam Penentuan Stok Barang Bangunan Melalui Pendekatan Machine Learning Adinda, Intan Bintang; Irawan, Davit; Karman, Joni; Sobri, Ahmad
Journal of Computer System and Informatics (JoSYC) Vol 7 No 1 (2025): November 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v7i1.8065

Abstract

This study aims to develop a machine learning-based construction material stock prediction model using a hybrid approach that combines K-Means Clustering as a sales pattern grouping method and Support Vector Machine (SVM) as a classification method to predict material sales levels. This research was motivated by the problem of stock management at Toko Usaha Jaya in Lubuklinggau City, which is still done manually, thus potentially causing excess stock that increases storage costs and stock shortages that can lead to lost sales opportunities and decreased customer satisfaction. The data used includes material names, initial stock quantities, quantities sold, remaining stock, and selling prices collected during the period from January to December 2023. The results show that the hybrid model is capable of grouping materials into three categories, namely very popular, fairly popular, and less popular, with a Silhouette Score of 0.42, indicating fairly good clustering quality. Furthermore, the SVM model produced a classification accuracy rate of 99%, reflecting an increase in stock prediction accuracy compared to manual management methods. These findings indicate that the application of the K-Means and SVM hybrid model can improve inventory management efficiency and support more accurate and effective data-driven decision making.
Penerapan Metode ORESTE untuk Pemilihan Smartphone Gaming Terbaik di Kelas Menengah Berdasarkan Konsumen Sinuhaji, Samantha Arta; Hardiansyah, Sandy; Simanjuntak, Candra Harapan; Saifullah, Saifullah
Journal of Computer System and Informatics (JoSYC) Vol 6 No 4 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i4.8093

Abstract

This study aims to help consumers choose the best mid-range gaming smartphone using the ORESTE (Organisation, Élimination et Choix Traduisant la Réalité) method. This method is applied to provide recommendations based on important criteria, such as processor performance, RAM capacity, screen quality, battery life, and price. The first stage of this study is problem identification that focuses on choosing the best mid-range gaming smartphone. Next, the determination of alternatives and relevant criteria is carried out, followed by data collection using a questionnaire distributed to 78 respondents. The collected data is then analyzed using the ORESTE method to calculate the global ranking of each alternative. The results of the analysis show that Samsung smartphones are in first place as the best choice based on the ranking obtained from each criterion. The use of the ORESTE method allows systematization in decision-making, by considering the weight and priority of relevant criteria. This study contributes to helping consumers make more objective and targeted decisions in choosing a gaming smartphone that suits their needs and preferences. In addition, this study is also expected to be a reference for smartphone manufacturers in understanding consumer preferences and improving the quality of their products. Thus, the results of this study are not only beneficial for consumers, but also for the smartphone industry as a whole.
Penerapan Metode MABAC untuk Menentukan Café Tongkrongan Anak Muda Terbaik Suratman, Amanda Putri; Larasati, Larasati; Anggiani, Poppy Hanna; Batubara, Ela Roza
Journal of Computer System and Informatics (JoSYC) Vol 6 No 4 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i4.8258

Abstract

Cafés serve not only as a place to enjoy coffee, but also as a forum for social interaction, discussion, and relaxation, especially for the younger generation who are increasingly embracing cafes as part of their lifestyle. In this context, choosing the right cafe is crucial so that the experience is not only satisfying in terms of taste, but also in terms of comfort and atmosphere. This study aims to determine the best cafes for gatherings in Pematangsiantar City using the Multi-Attributive Border Approximation Area Comparison (MABAC) method in a Decision Support System (DSS). This method is used because it can handle decision-making involving multiple criteria objectively and systematically. Seven main criteria were analyzed: price, taste, facilities, atmosphere, service, quality of ingredients, and cleanliness. A total of seven popular cafes in the city were evaluated based on these criteria. The calculation results showed that Inhaven Specialty Coffeebar obtained the highest score of 1.1456829 and was named the best cafe. These findings prove that the MABAC method can provide accurate recommendations and help consumers, especially young people, in choosing an ideal and enjoyable gathering place.
Sentiment Analysis of Tokopedia Customer Reviews using IndoBERT and SMOTE for Class Imbalance Handling Saputra, Imam; Mesran, Mesran; Ginting, Guidio Leonarde
Journal of Computer System and Informatics (JoSYC) Vol 7 No 1 (2025): November 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v7i1.8748

Abstract

Sentiment analysis in the Indonesian e-commerce sector faces significant challenges due to the informal nature of language and severe class imbalance, where neutral reviews are often underrepresented. This research proposes a hybrid framework combining the deep semantic capabilities of IndoBERT with the Synthetic Minority Over-sampling Technique (SMOTE) to improve classification fairness. Using a dataset of Tokopedia customer reviews, this study compares a baseline model against a balanced model using SMOTE on 768-dimensional IndoBERT features. The experimental results reveal that while the baseline model achieved a high overall accuracy of 83%, it suffered from an "accuracy paradox," exhibiting a dismal recall of only 0.07 for the neutral class. Upon implementing SMOTE, the neutral class recall surged to 0.29, marking a significant 314% improvement in minority class detection. Although overall accuracy slightly decreased to 81%, the Macro Average F1-Score increased from 0.61 to 0.65, proving that the model is more robust and objectively reliable across all sentiment polarities. This study demonstrates that sacrificing marginal accuracy for improved minority sensitivity is vital for providing accurate business intelligence in the digital marketplace. These findings provide a robust roadmap for developing more equitable automated sentiment analysis systems in Indonesia.
Analisis Sentimen Penggunaan Cekat.AI dalam Menggantikan Customer Service Menggunakan Logistic Regression dan TF-IDF Ardyaputra, Gede Yudha; Pascima, Ida Bagus Nyoman; Suputra, Putu Hendra
Journal of Computer System and Informatics (JoSYC) Vol 7 No 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v7i2.9292

Abstract

The rapid development of artificial intelligence has significantly transformed customer service systems, particularly through the use of chatbots to replace human customer service agents. Cekat.AI is one of Indonesia’s local AI-based chatbot innovations that has been increasingly adopted by companies. However, its implementation has generated diverse public reactions on social media platforms, especially X and TikTok. The main problem addressed in this study is how users perceive and respond sentimentally to the use of Cekat.AI as a replacement for human customer service, as well as the underlying factors influencing these ssentiments This study aims to analyze public sentiment using the Logistic Regression method with Term Frequency–Inverse Document Frequency (TF-IDF) feature extraction on social media comments from X and TikTok. To address class imbalance in sentiment data, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The results indicate that negative sentiment dominates at 52.5%, followed by positive sentiment at 35.1% and neutral sentiment at 12.4%. The implementation of SMOTE significantly improved the Recall of the neutral class from 18.6% to 64.1%, with a cross-validation accuracy of 79.98%. Topic modeling further reveals that negative sentiment is primarily driven by automation anxiety and concerns over job displacement. These findings suggest that the main challenge in adopting Cekat.AI lies in social acceptance rather than technical performance. This study provides a dual contribution, namely technically proving the effectiveness of SMOTE in handling extreme imbalance in Indonesian text data, and practically revealing that public resistance to local AI is rooted in job displacement anxiety, not merely technical service aspects.
Segmentasi Pelanggan Menggunakan K-Means Clustering Pada Data Transaksi Online Retail Robby Satria Darma; Lukman Sunardi; Asep Toyib
Journal of Computer System and Informatics (JoSYC) Vol 7 No 1 (2025): November 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v7i1.8189

Abstract

The telecommunications industry faces challenges in understanding customer characteristics due to large data volumes and diverse service usage behaviors. Customer segmentation becomes a strategic approach to support more targeted and effective marketing strategies. This study aims to apply the K-Means clustering algorithm to perform data-driven customer segmentation in a telecommunications company. The customer dataset undergoes preprocessing stages including missing value handling, categorical variable encoding using One-Hot Encoding, and feature scaling with StandardScaler. The optimal number of clusters is determined using the Elbow Method. The results show the formation of three customer segments with distinct characteristics based on tenure, monthly charges, total charges, and churn rate. Visualization using Principal Component Analysis (PCA) clearly illustrates the separation among clusters. An interesting finding reveals that the segment with the highest customer value also has the highest churn rate, indicating the need for more specific retention strategies. The contribution of this study lies in providing an unsupervised learning-based customer segmentation approach that can assist companies in designing more effective marketing strategies, improving customer retention, and supporting data-driven decision making.

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