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Jurnal Sistem Informasi dan Informatika (SIMIKA)
Published by Universitas Banten Jaya
ISSN : 26226901     EISSN : 26226375     DOI : 10.47080
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
Jurnal Sistem Informasi dan Informatika aims to provide scientific literature specifically on studies of applied research in information systems (IS), information technology (IT) and public review of the development of theory, method, and applied sciences related to the subject.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 181 Documents
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APLIKASI PROFILING KEBUTUHAN PELAJARAN TAMBAHAN SISWA SMA MENGGUNAKAN ALGORITMA RANDOM FOREST Nabiel Fauzan Ramadhan; Lathifah Alfat
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 1 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i1.4094

Abstract

This study presents EduTrack, a profiling application that uses a Random Forest classifier to identify Indonesian high-school students’ needs for additional Math tutoring. The dataset consists of students’ chapter-wise Math scores, processed with Pandas and Scikit-learn and stored via SQLAlchemy. The backend is implemented in Flask, while the frontend employs Bootstrap with Chart.js for charts and DataTables for tabular display. Dummy evaluation yields model performance around 90% accuracy, with precision 88%, recall 91%, and F1-score 89% (Table 1, Figure 2). Evaluation metric formulas (precision = TP/(TP+FP), recall = TP/(TP+FN), F1 = 2 * precision * recall / (precision + recall)) are included for clarity. EduTrack is designed not only as a predictive tool, but also as a practical decision-support system for teachers. By visualizing student performance at the chapter level, the application enables educators to identify learning gaps more intuitively and implement timely interventions. This helps shift teaching strategies from reactive to proactive, ultimately supporting personalized learning and improving academic outcomes.
FORECASTING PERSEDIAAN STOK TOKO REZA SUKSES DENGAN METODE DES eka eka; William Ramdhan; Wan Mariatul Kifti inti
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 1 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i1.4108

Abstract

The development of information technology offers significant opportunities for businesses to improve operational efficiency, including inventory management. Toko Reza Sukses, a clothing retail business, faces challenges in balancing stock levels with customer demand. This imbalance can lead to overstocking or stockouts, resulting in losses and decreased customer satisfaction. This study aims to apply the Double Exponential Smoothing (DES) method to forecast stock requirements based on historical data. DES was chosen for its ability to capture upward or downward trends in sales data. The system is designed as a web-based application using the PHP programming language to facilitate an effective and efficient forecasting process. The implementation results show that the system is capable of providing more accurate stock estimates, reducing instances of stock shortages and surpluses, and enhancing customer satisfaction through better product availability.
OPTIMASI PEMILIHAN QUALITY ASSURANCE CV FORTUNE CLEAN MENGGUNAKAN METODE TOPSIS Annisa Putri Gita Cahyani; Naurah Huwaida; Gandung Triyono
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 1 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/dxeh2n20

Abstract

CV Fortune Clean, a company providing cleaning services and products, requires Quality Assurance (QA) professionals to ensure the optimal performance of technological and system updates in its internal applications, which are vital for business processes such as inventory management and service scheduling. The previous recruitment process, reliant on manual CV screening and subjective interviews, took up to four months to identify truly competent candidates, causing delays in application updates and potentially hindering operational efficiency. To address this issue, this study designs a Decision Support System (DSS) based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. TOPSIS was chosen for its ability to evaluate candidates based on their proximity to an ideal solution, considering technical criteria and non-technical criteria (e.g., problem-solving and communication skills). The DSS implementation reduced recruitment time from four months to one month, enhanced selection accuracy by minimizing subjective bias, and proved more consistent than manual methods in comparative simulations. The TOPSIS system also improved transparency and objectivity in the selection process, optimizing recruitment duration and enhancing the quality of QA personnel to support the reliability of internal applications critical to business operations.
PERANCANGAN ENTERPRISE ARCHITECTURE SISTEM INFORMASI ARSIP KPP PRATAMA SUKABUMI DENGAN PENDEKATAN ZACHMAN FRAMEWORK Riska Fitria Rahmat; Arny Lattu; Anton Permana
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 1 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i1.4116

Abstract

Sukabumi Pratama Tax Service Office (KPP) is a government agency that has a high need for efficient archive management. However, the archiving process that is still carried out conventionally causes obstacles in searching, monitoring, and managing documents. To answer these problems, an Enterprise Architecture of an archiving information system was designed using the Zachman Framework approach, which involves six perspectives consisting of Planner, Owner, Designer, Builder, Sub-Constructor, and Functioning System and six columns including What, How, Where, Who, When, and Why. This study developed a system concept and user interface design into a prototype by applying the Zachman Framework across all perspectives. The result is a design for an archiving system that aligns with the organizational structure and operational workflow of the Tax Office. Testing of the prototype was carried out using the System Usability Scale (SUS) method invoiling 7 respondents from 6 departmen, resulting  in an average score of 90 which is classified as Grade A (excellent) indicating that the system can be well received by users, is well structured, and is ready to be implemented into a functional system to support the archiving process in the Sukabumi Pratama Tax Service Office (KPP) environment.
PENERAPAN E-CRM DALAM MENINGKATKAN OMZET PAKAIAN ANAK DAN DEWASA DI TOKO WULAN BUSANA  Ade Nurainun; William Ramdhan; Wan Mariatul Kifti wan mariatul
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 1 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i1.4124

Abstract

The advancement of information technology has encouraged business actors, including retail stores such as Wulan Busana, to adopt more effective systems for managing customer relationships. One relevant solution is the implementation of Electronic Customer Relationship Management (e-CRM) to enhance customer loyalty and satisfaction. This study aims to design and implement a web-based e-CRM system at Wulan Busana store using the CodeIgniter framework. The method used is qualitative, with data collection techniques including observation, interviews, and literature review. The results of the study show that the e-CRM system helps the store manage customer data, monitor product stock, and carry out marketing activities in a more structured and efficient manner. The implementation of this system has proven to enhance interaction between the store and customers, accelerate responses to customer needs, and simplify promotional and sales evaluation processes. The system also has a positive impact on increasing customer loyalty and improving the store’s operational efficiency.
IMBALANCED DATA HANDLING FOR OPTIMIZING RANDOM FOREST IN SENTIMENT ANALYSIS OF EAST JAVA GUBERNATORIAL ELECTION Rahma Putri Widyaiswari; Anisa Dzulkarnain; Alqis Rausanfita
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 1 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i1.4131

Abstract

Social media has become a strategic platform in conveying public opinion, especially at the moment of the Regional Head Election (Pilkada). The large amount of opinion data produced opens up opportunities for the application of sentiment analysis to map public perception. One of the main challenges in the classification of sentiment is the imbalance of distribution between classes, which can degrade the accuracy of the model, especially in recognizing minority classes. This study aims to analyze the impact of the application of data balancing techniques on the performance of the 2024 East Java Regional Election sentiment classification model using the Random Forest algorithm. The series of processes in the study include data preprocessing, manual sentiment labeling, text preprocessing, word weighting with TF-IDF, and model training on three data ratios, namely 90:10, 80:20, and 70:30. Each ratio was tested in three scenarios, namely no balancing (baseline), undersampling using the Tomek Links method, and oversampling using Borderline-SMOTE. Of all scenarios, Borderline-SMOTE gave the highest accuracy of 82.40% at an 80:20 ratio, an increase of 2.19% compared to the unbalanced condition at the same ratio. These results show that data balancing is able to improve the performance of the model in classifying sentiment more proportionally.
ANALISIS KLASIFIKASI PENYAKIT DIABETES DENGAN SUPPORT VECTOR MACHINE (DATA KAGGLE) Nur Nafiiyah; Arif Try Hidayatullah
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 1 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/v1tx1m90

Abstract

Diabetes, also known as diabetes mellitus, is a long-term condition caused by the inability of the pancreas to produce enough insulin, which leads to increased levels of glucose in the blood. Diabetes is a dangerous disease. There is no known cause of diabetes, but many believe that lifestyle and genes may play a role. Bioinformatics researchers are trying to overcome this disease and create systems that help predict diabetes. Many diabetes prediction systems use methods such as C4.5, KNN, Naive Bayes, and linear SVM, according to existing research. In this study, the analysis of the accuracy of diabetes disease data classification was carried out using SVM and several choices of variables on the original and balanced data. The results of the original data experiment with 768 rows of variables that have the highest correlation are glucose, and using three variables (glucose, BMI, Age) has the highest accuracy with SVM RBF and Polynomial (0.773). Balanced data using five variables (pregnancies, glucose, BMI, diabetes pedigree function, age) has the highest classification accuracy of linear SVM (0.775). Conclusion: by balancing the number of diabetes disease classes, there is a slight increase in classification accuracy results from the initial 0.766 to 0.775.
COMPARISON OF SPLIT DATA RATIO PERFORMANCE IN SENTIMENT ANALYSIS OF PON XXI ACEH-SUMUT 2024 USING SUPPORT VECTOR MACHINE WITH SMOTE APPLICATION Karina Shafa Amalia; Anisa Dzulkarnain; Berlian Rahmy Lidyawati
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 1 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i1.4161

Abstract

The 21st National Sports Week (PON) Aceh-North Sumatra 2024 is the largest multi-sport competition in Indonesia, sparking diverse public responses on social media platforms, particularly X (formerly Twitter). The high volume and diverse nature of comments related to PON XXI pose challenges in understanding public sentiment and communication patterns. This study aims to compare the performance of various training and testing data splitting ratios in the Support Vector Machine (SVM) algorithm with an RBF kernel for sentiment classification of X platform data related to PON XXI Aceh-North Sumatra 2024. The research methodology involved data collection using the Tweet Harvest library, gathering 2,503 Indonesian-language posts during the period from 9 August to 20 October 2024. Text preprocessing included cleaning, case adjustment, normalisation, tokenisation, stop word removal, and stemming. The dataset was classified into three sentiment categories: positive, negative, and neutral. Four different split ratios were evaluated: 90:10, 80:20, 70:30, and 60:40. The SMOTE (Synthetic Minority Over-sampling Technique) was applied to address the class imbalance issue. The results show that the 80:20 split ratio achieved optimal performance with the highest accuracy of 86.23%, precision of 86.10%, recall of 86.23%, and F1 score of 86.15%. These findings indicate that the appropriate data split ratio significantly influences model performance and provides valuable insights for developing more accurate and representative public opinion analysis models for Indonesian social media content.
MODEL PREDIKSI PRODUKTIVITAS PADI MENGGUNAKAN XGBOOST DAN RANDOM FOREST Yoga Safitra Anugraha; Helda Yenni; Wirta Agustin; Hadi Asnal
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 1 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i1.4169

Abstract

Rice is a strategic commodity in ensuring national food security in Indonesia. Predicting rice productivity is a critical issue due to the decreasing harvest area and fluctuating production. This study aims to develop and compare the performance of two machine learning algorithms, namely Extreme Gradient Boosting (XGBoost) and Random Forest, in predicting rice productivity based on harvest area and total production data. The dataset consists of rice productivity data from 38 provinces in Indonesia over the period 2018 to 2024. The models were evaluated using three data splitting ratios (70:30, 80:20, and 90:10) and four evaluation metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²). The results show that both models perform well, with Random Forest achieving the highest R² value of 0.887 and the lowest RMSE of 2.939 on the 90:10 split, indicating higher accuracy. XGBoost, while slightly lower in accuracy (R² = 0.781), produced more stable predictions across varying input scales. When tested on new data, both models showed consistent performance, demonstrating generalization capabilities. These findings indicate that machine learning models are effective in modeling and forecasting agricultural productivity and can serve as decision-support tools for policymakers and agricultural stakeholders. The models can be utilized for strategic planning, resource allocation, and improving agricultural productivity in the future.
DATA MINING APRIORI ALGORITHM TO ANALYZE BOOK RECOMMENDATIONS Sumiati Sumiati; Shella Wasilatul Hasanah
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 1 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i1.4327

Abstract

This study focuses on the development of a book recommendation system in the Library using the Apriori algorithm. By utilizing this algorithm, the library can analyze book borrowing transaction data to find patterns of student interest, which will help librarians in determining which books need to be recommended. Through the application of the Apriori algorithm, the system successfully identified 17 association rules that show the relationship between books that are often borrowed together. These rules have a minimum support of 5.5% and a confidence of 100%, indicating that this pattern is very strong and reliable. With the results of this study, it is hoped that librarians can be more efficient in recommending books to students, improving the reading experience, and maximizing the use of library collections. In the future, this system can be expanded by integrating user data for more personalized recommendations.  

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