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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 202 Documents
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PEMANFAATAN DATA MINING UNTUK IDENTIFIKASI POLA PEMBELIAN PRODUK PLATFORM PERDAGANGAN ELEKTRONIK E-COMMERCE PLAZA BANTEN Widyawati Widyawati; Eka Ramadhani Putra; Selly Septiani
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.v9i1.4454

Abstract

Digital transformation opens strategic opportunities for Micro, Small, and Medium Enterprises (MSMEs) to expand market reach through electronic commerce platforms. Plaza Banten is a digital marketplace that facilitates the promotion and sale of MSME products in Banten Province, Indonesia. Although transactions on the platform continue to increase, the utilization of transaction data for strategic decision-making has not yet been optimized. This study aims to identify consumer purchasing patterns on Plaza Banten through data mining by discovering product associations frequently purchased together and translating them into recommendations for promotions, product placement, and inventory planning. Sales transaction data were collected for a specific period and preprocessed through cleaning, transformation, and relevant attribute selection. The Apriori algorithm was applied in two scenarios: overall analysis and time-based transaction segmentation. Using a minimum support of 0.1% and minimum confidence of 60%, the analysis generated 8,117 association rules. The strongest rule achieved support = 0.348 and confidence = 98.9% (Nasi Box → Snack Box), while several segments reached confidence up to 100%. The highest lift value was 194.75 in the 06:00–09:00 segment, indicating highly specific co-purchase dependencies at certain times. These quantitative results reveal stable bundle patterns and time-dependent demand variations, supporting actionable strategies such as standardizing menu bundles, optimizing cross-selling offers, and prioritizing stock for high-correlation items. The resulting rules are interpreted and visualized to support Plaza Banten administrators and MSME partners in implementing data-driven decisions and strengthening the digital economy ecosystem in Banten Province.
PROTOTYPE SISTEM MONITORING KETINGGIAN AIR DAN CURAH HUJAN DI BENDUNGAN CIKALUMPANG BERBASIS INTERNET OF THINGS (IOT) Ramdani Budiman; Tifani Intan Solihati; Khasan Asrori; Iman Faturohman; Raden Kania
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.v9i1.4609

Abstract

Cikalumpang Dam, located in Kadubeureum Village, Padarincang District, Serang, Banten Province, is an irrigation area under the authority of Banten Province and managed by the Ciujung-Cidanau Watershed Management Unit UPTD (PDAS). Rising water levels in the dam, influenced by heavy rainfall and high river discharge, have become a recurring issue. Currently, water level measurement is conducted manually using conventional tools, causing delays in delivering crucial information to officers. Such delays increase the risk of flooding, which can damage infrastructure, harm the environment, and threaten human safety. The 2023 flood in Citasuk Village illustrates the risks caused by inadequate monitoring systems. This research applies a prototype method to develop an Internet of Things (IoT)- based water level and rainfall monitoring system integrated with the Blynk application. The system employs an ultrasonic sensor to measure water levels and a rainfall sensor to monitor precipitation in real-time, with data sent directly to the Blynk platform for monitoring and notification. The development stages include requirement analysis, prototype construction, system evaluation, coding, and testing. The system is designed to provide rapid alerts, enabling timely intervention to reduce flood risks. The main objective of this research is to improve water resource management efficiency at Cikalumpang Dam through modern technology implementation. Accurate and responsive monitoring is expected to minimize the negative impacts of flooding and enhance public safety. This study contributes to the development of intelligent solutions for water resource management and disaster mitigation.
STRATEGI PENGEMBANGAN WEBSITE SEBAGAI MEDIA PROMOSI DAN BRANDING DI PONDOK PESANTREN TURUS PANDEGLANG Mohamad Hilman; Sindanita Yulianty; Holilah Holilah; Royan Habibie Sukarna; Fitri Damyati
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (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.v9i2.4325

Abstract

Turus Islamic Boarding School faces challenges in maximizing its promotional and branding potential as an educational institution. One effective way to increase exposure and attract attention is through the development of an impactful website. This study aims to analyze the strategy of developing a website as a promotional and branding media for Turus Islamic Boarding School. The method used is a case study, observing the website development process, content management, and its impact on the institution's image. The results indicate that a well-structured and informative website can enhance the visibility of the Turus Islamic Boarding School, introduce educational programs, and establish better relationships with the community and prospective students. A user-friendly website with appealing design also plays a crucial role in strengthening the pesantren’s branding as a modern and trustworthy institution. This study suggests that Turus Islamic Boarding School continue to develop its website by addressing visitor needs and utilizing the latest technology to improve interaction and attractiveness.
IMPLEMENTASI SISTEM DETEKSI PENYAKIT DAUN JAGUNG MENGGUNAKAN CNN DENGAN FINE-TUNING HYPERPARAMETER Amir Nugroho; Agus Iskandar
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (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.v9i2.4507

Abstract

Maize is a strategic food commodity in Indonesia; however, its productivity is often reduced by leaf diseases such as Common Rust, Gray Leaf Spot, and Bacterial Leaf Streak. Manual identification is subjective and prone to error due to the visual similarity of symptoms. This study aims to develop an automated maize leaf disease detection system using a Convolutional Neural Network (CNN) based on the MobileNetV2 architecture with a hyperparameter fine-tuning approach. The model was trained and evaluated using the Seasonal Corn Leaf Disease Dataset consisting of 2,943 images across five classes. Data preprocessing included resizing to 224×224 pixels, normalization, and stratified data splitting. The experiment focused on evaluating three augmentation strength levels (0.15, 0.25, and 0.35) using a two-phase training strategy. The results show that the optimal configuration was achieved at augmentation strength 0.25, producing an accuracy of 97.51% and an F1-score of 0.971 on 422 test images. The confusion matrix analysis indicates that most misclassifications occur between visually similar classes, particularly Common Rust and Gray Leaf Spot. The best-performing model was successfully deployed in a Flask-based web application, enabling real-time disease detection with an average inference time of 1.8 seconds per image. These findings demonstrate that appropriate augmentation strength significantly improves model generalization and that the proposed system provides a practical and accessible solution for supporting smart agriculture in Indonesia.
ANALISIS STATISTIK PENGUKURAN DINI CYBERSICKNESS MENGGUNAKAN SIMULATOR SICKNESS QUESTIONNAIRE (SSQ) DENGAN STIMULUS VIDEO GAME 3D “MIRROR EDGE” Alfi Zuhriya Khoirunnisaa; Bagas Diki Saputra
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (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.v9i2.4512

Abstract

Cybersickness arises from prolonged 3D visual exposure, marked by nausea, dizziness, eye strain, and imbalance. To detect its early onset, the Simulator Sickness Questionnaire (SSQ) was applied using the 3D game Mirror’s Edge as stimulus in two scenarios: active game playing and passive watching. The exposure duration was set to 20 minutes because previous studies indicate that cybersickness symptoms generally begin to appear after approximately more than 10 minutes like feel immersive or exposure in Virtual Reality. The average SSQ scores for active game playing were pre-SSQ = 161.18 and post-SSQ = 553.56 (9 subjects), while the average SSQ scores for passive watching were pre-SSQ = 79.98 and post-SSQ = 1144.8 (4 subjects). Based on the SSQ results, it can be concluded that all research subjects experienced an increase in cybersickness symptoms. SSQ’s score from passive watching is higher than SSQ’s score active game playing. This provides evidence of sensory conflict occuring. To enhance data validity, statistical tests were conducted using the Shapiro–Wilk test and paired t-test. The statistical results for the active game playing scenario showed p > 0.11 (p-value > 0.05) for the Shapiro–Wilk test and p-value < 0.005774 (α = 0.05) for the paired t-test. Meanwhile, for the passive watching scenario, the statistical test results indicated p-value > 0.100 for the Shapiro–Wilk test and p-value < 0.034 (α = 0.05) for the paired t-test. From both statistical tests, it can be concluded that all data were normally distributed and there was a highly significant difference between pre-stimulus and post-stimulus conditions. Thus, the designed research scenarios can be considered to have high validity. These results can serve as a reference for game developers and VR designers in developing adaptive motion systems, reducing excessive visual motion, and implementing appropriate exposure duration limits to improve user comfort and safety. Furthermore, the findings may assist VR users in recognizing early cybersickness symptoms and adopting preventive strategies such as periodic rest intervals during prolonged gameplay sessions.
AN END-TO-END CRNN AND CTC APPROACH FOR OFFLINE HANDWRITTEN CHINESE TEXT RECOGNITION Stefanus Eko Prasetyo; Jeffrey; Haeruddin
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (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.v9i2.4519

Abstract

Offline handwritten Chinese text recognition remains a challenging problem due to the large number of character classes, complex character structures, and high variability in writing styles. This paper proposes an end-to-end offline handwritten Chinese text recognition system based on a CNN–BiLSTM–CTC architecture. A convolutional neural network (CNN) is used to extract spatial features from handwritten text images, while a bidirectional long short-term memory (BiLSTM) network captures contextual dependencies between characters in both forward and backward directions. The Connectionist Temporal Classification (CTC) framework is applied to enable segmentation-free training and decoding of character sequences directly from input images. Experiments conducted on the CASIA-HWDB 2.2 offline handwritten Chinese text dataset demonstrate that the proposed approach achieves a Character Error Rate (CER) of 16,85%, corresponding to an accuracy of 83,15%, confirming the effectiveness of the CNN–BiLSTM–CTC framework for offline handwritten Chinese text recognition.
KLASIFIKASI KEMATANGAN BUAH KELAPA SAWIT BERBASIS TRANSFER LEARNING CONVOLUTIONAL NEURAL NETWORK Nur Nafiiyah; Mohamad Habib Havito Ihzami; Agus Harjoko; Achmad Nizar Hidayanto
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (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.v9i2.4539

Abstract

The ripeness level of oil palm fruit directly affects the quality and yield of palm oil, making accurate and objective classification methods essential. This study aims to evaluate and compare the performance of several transfer learning Convolutional Neural Network (CNN) architectures for oil palm ripeness classification. The dataset used is a secondary dataset obtained from previous research and consists of four ripeness classes: under-ripe, unripe, ripe, and over-ripe. Data augmentation was applied only to the training data to increase data variability, while the original, non-augmented data were used for testing to ensure an objective evaluation. Seven CNN architectures were evaluated, namely ConvNeXt-Tiny, DenseNet121, InceptionV3, MobileNetV2, NASNetLarge, ResNet50, and Xception, using the same training configuration. The results show that ConvNeXt-Tiny and ResNet50 achieved the best performance, with accuracy and F1-scores of 97.73%. These findings indicate that efficient CNN architectures can provide optimal performance for oil palm ripeness classification.  
RANCANG BANGUN APLIKASI E-KONSELING BERBASIS WEB DENGAN METODE CERTAINTY FACTOR Muhammad Kevin Al Khahfi; Diana Diana
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (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.v9i2.4542

Abstract

Incompatibility between a chosen major and students’ interests and talents is often a primary obstacle to academic success in higher education. At the same time, the availability of guidance and counseling teachers for personal assistance remains highly limited. This Research aims to design and develop a web-based e-counseling expert system application capable of automatically identifying major recommendations based on students’ self-potential at SMA Quraniah Palembang. The Research method employed is the Expert System Development Life Cycle (ESDLC) with qualitative approaches. Data collection techniques were conducted through observation, literature review, and in-depth interviews with an expert, specifically a Guidance and Counseling teacher, to acquire the knowledge base. The system was developed using the PHP programming language with the Laravel framework, a MySQL database, and a responsive interface built with Bootstrap. To address diagnostic uncertainty, this study implemented the Certainty Factor (CF) method by combining expert Measure of Belief (MB) and Measure of Disbelief (MD) values and user certainty values. The results indicate that the E-Counseling application accurately performs the identification function, producing outputs as percentage confidence levels for specific types of intelligence and relevant majors. Black-box testing confirmed that all functional features of the system operate correctly in accordance with the design specifications. In conclusion, this application is an effective decision-support tool for students in planning their academic futures. The implication of this Research is the availability of a digital platform that facilitates the efficient identification of student potential without time and place constraints, serving as a foundation for future technology-based counseling development.
ADALINE NEURAL NETWORK UNTUK PREDIKSI CO2 DALAM RUANGAN Dwi Marisa Midyanti; Syamsul Bahri; Zalikhah Khairunnisa; Hafizhah Insani Midyanti
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (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.v9i2.4561

Abstract

Indoor carbon dioxide (CO2) concentrations can negatively impact human health, making it essential to predict their hazardous levels. This study applies the Adaline Neural Network, an artificial neural network algorithm, to predict the classification of CO2 as dangerous or non-hazardous indoors. The main focus of this study is to explore the use of early stopping parameters with patience values ​​to minimize the number of iterations required in the training process. The data used in this study consisted of 596 training data, 85 validation data, and 170 test data. Based on the experimental results, Adaline Neural Network achieved an accuracy of 97.06% on the test data using a learning rate of 0.02. In addition, observations of the early stopping parameters showed that there was no significant change in the validation Mean Squared Error (MSE) value and the test data classification results even though using 5 and 10 patience and 500 iterations. These findings indicate that the use of early stopping can speed up the algorithm's stopping process at the eighth iteration without reducing prediction performance.
LOCAL IRRADIANCE PREDICTION USING HYBRID CNN–LSTM WITH FEATURE SELECTION Irfan Gustia Rahman; Rila Mandala
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (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.v9i2.4584

Abstract

Solar energy is one of the most sustainable renewable energy sources, and its utilization through photovoltaic (PV) systems is highly dependent on the accuracy of solar irradiance prediction. However, predicting solar irradiance remains challenging due to the nonlinear relationship between weather variables and solar cell data, as well as the high dimensionality of input features. Therefore, this study aims to develop an efficient prediction model using a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture combined with SelectKBest feature selection. Two heterogeneous datasets, consisting of solar cell data and weather data, were integrated and processed through preprocessing, Z-score normalization, and feature engineering, resulting in 28 input features. Subsequently, F-regression-based SelectKBest was applied to select 13 of the most relevant features. The performance of the CNN–LSTM model with and without feature selection was evaluated using Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), coefficient of determination (R²), and training time. The results show that feature selection reduced training time from 151.14 seconds to 138.32 seconds and improved MAPE from 45.10% to 42.86%. However, RMSE increased from 69.46 to 73.27 and R² slightly decreased from 0.937 to 0.930. These findings indicate that SelectKBest can improve computational efficiency while maintaining acceptable predictive performance in solar irradiance forecasting.

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