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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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IMPLEMENTASI ALGORITMA FP-GROWTH PADA PENEMPATAN PRODUK DI PRIMA DEWATA Novem Berlian Uly; Murry Albert Agustin Lobo; Nyongki Daku Ali Hary; Yupiter Yiwa Hinggiranja
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): 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.v8i2.3739

Abstract

Effective product placement in a mini market or retail store is an important factor in increasing sales and customer satisfaction. The FP-Growth algorithm is able to identify patterns or relationships of products that are often purchased together, so that it can help in more strategic product arrangement. Prima Dewata faces challenges in optimizing product placement to attract customer attention and maximize available space. This study aims to identify patterns of product relationships that are often purchased together, so that related products can be strategically placed to attract consumer attention. This study begins with data collection, pre-processing, implementation of the FP-Growth Algorithm and analysis then results. The results obtained are that Sariwangi tea products are the most purchased products by Prima Dewata visitors in the period January - November 2024 and there are 4 main rules that can be used as a reference in compiling goods based on data processing results, namely 1. Sariwangi tea and Clear Men Shampoo (support: 0.682 and confidence: 0.867); 2. Sariwangi tea and Indomie Pepsodent Toothpaste (support: 0.171/ confidence: 0.876); 3. Sariwangi tea and Indomie Ayam Bawang (support: 0.175/ confidence: 0.922); 4. Sariwangi tea and Lifebuoy Soap (support: 0.166/ confidence: 0.937).
PREDIKSI HARGA SAHAM DI INDONESIA DENGAN EXTREME GRADIENT BOOSTING YANG DIOPTIMALKAN OLEH ADAPTIVE PARTICLE SWARM OPTIMIZATION Alya Mirza Safira; Trimono Trimono; Kartika Maulida Hindrayani
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: 10.47080/5r67ag12

Abstract

Volatile stock price movements are a big problem in making investment decisions, especially in stocks with high volatility. This research aims to build a stock price prediction model by utilising the Extreme Gradient Boosting (XGBoost) algorithm optimised using the Adaptive Particle Swarm Optimization (APSO) method. The research focuses on two stocks with high volatility levels, namely PT Jaya Agra Wattie Tbk (JAWA) and PT Charnic Capital Tbk (NICK) with historical closing price data from August 2019 to July 2024. The research process includes data collection, preprocessing, modelling, optimazing and model performance evaluation using the Mean Absolute Percentage Error (MAPE) metric. The results showed that the XGBoost-APSO combination proved superior to the standard XGBoost-PSO and XGBoost methods in predicting stock prices without overfitting. The MAPE value on JAWA stock is 5.20 (training) and 5.95 (testing) with a difference of 0.75. As for NICK stock, the MAPE on training data is 4.50 and testing is 5.40, with a difference of 0.90. The model also successfully predicts the closing price movement in the next five days realistically according to its historical volatility characteristics. This research proves that the combination of XGBoost with APSO optimisation is effective in handling stock data with high volatility and can be used as a predictive tool in investment decision making. Keyword: prediction, stock price, volatile, XGBoost, APSO Data, Source Code, dan Plagiarisme: https://drive.google.com/file/d/1GRhEguXHYj-mzbE2fW7MsnpjUuaJnyxl/view?usp=sharing
IDENTIFIKASI POLA DAN KARAKTERISTIK IKM DI KOTA SURABAYA MENGGUNAKAN METODE BIRCH DENGAN EVALUASI SILHOUETTE SCORE Fitri Indah Sari; Mohammad Idhom; Trimono
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.v9i1.4001

Abstract

Small and Medium Industries (SMIs) play a crucial role in regional economic development, particularly in metropolitan areas such as Surabaya, Indonesia. Nevertheless, the high heterogeneity of SMI characteristics poses challenges for designing effective and targeted development policies. This study proposes a data-driven clustering framework to identify patterns and characteristics of SMIs in Surabaya by employing the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm. The novelty of this research lies in the application of BIRCH for large-scale SMI data clustering combined with systematic parameter evaluation using the Silhouette Score to ensure cluster quality and stability. BIRCH was chosen for its efficiency in handling large and heterogeneous datasets through hierarchical summarization, addressing limitations of methods such as K-Means that require predefined cluster numbers and are sensitive to initial centroids. The dataset was preprocessed through missing value handling, data type transformation, categorical label encoding, and numerical standardization. After preprocessing, 31,472 records with six variables were analyzed. Various combinations of threshold and branching factor parameters were evaluated using the Silhouette Score to determine the optimal configuration. The best result was obtained with a threshold of 0.7 and a branching factor of 50, achieving a Silhouette Score of 0.743 and forming five distinct clusters. The resulting clusters exhibit clear structural patterns in terms of land area, initial capital, labor force, business scale, company type, and risk level. The findings demonstrate that BIRCH effectively produces well-separated and interpretable clusters, providing a robust analytical basis for evidence-based policymaking in SMI development.
PENGEMBANGAN APLIKASI TERINTEGRASI BERBASIS WEIGHTED MOVING AVERAGE DAN METODE AVERAGE UNTUK PERAMALAN PERSEDIAAN DAN HARGA POKOK PENJUALAN Triana Sri Gunarti; Baibul Tujni; Imam Solikin
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.v9i1.4004

Abstract

This study aims to design and implement an integrated application for inventory forecasting using the Weighted Moving Average (WMA) method and cost of goods sold calculation using the Average method at TB Amanda, a company engaged in building material sales. The WMA method is applied to forecast future inventory requirements based on weighted historical sales data, while the Average method is used to calculate the cost per unit of inventory based on the weighted average of available stock costs. The system was developed using a structured software development approach and evaluated through functional testing and accuracy testing of forecasting results. The evaluation shows that the implemented WMA method produces forecasting results with a good level of accuracy, while the Average method generates cost of goods sold values that are consistent with manual calculations. In addition, the integrated system is able to automate transaction recording, inventory updating, forecasting, and cost calculation in a single database. These results indicate that the proposed application can support inventory planning and cost calculation processes at TB Amanda more systematically and accurately.
EVALUASI USER EXPERIENCE APLIKASI BANK JAGO MENGGUNAKAN USER EXPERIENCE QUESTIONNAIRE (UEQ) Muhimmatul Faizah; Eki Saputra; Megawati Megawati; Mona Fronita
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.v9i1.4019

Abstract

Digital transformation in the banking sector is essential to meet evolving customer expectations and compete with advances in financial technology (fintech). Bank Indonesia's report shows digital banking transaction growth of 34.49% year-on-year in Q2/2024, reflecting the increasing adoption of digital services. As one of the five fully digital banks in Indonesia, Bank Jago must ensure a seamless user experience to remain competitive. This study evaluates the user experience of the Bank Jago app using the User Experience Questionnaire (UEQ). The results showed that all dimensions received positive ratings, with attractiveness getting the highest score of 1,673, followed by perspicuity and stimulation. However, efficiency, dependability, and novelty received lower ratings. The comparative analysis places Bank Jago in the “Good” category for attractiveness and stimulation, “Above Average” for perspicuity and novelty, and “Below Average” for efficiency and dependability. Interview results support these findings, with users expressing concerns about system speed, security, complaint response time, and real-time notifications. Therefore, while the user experience is generally positive, focused improvements are needed to enhance usability and maintain competitiveness in the digital banking sector.
PENERAPAN RELEVANCE-AUGMENTED GENERATION LARGE LANGUAGE MODELS UNTUK SUMMARISE BERITA PADA PORTAL BERITA DETIKNEWS Khananda Raihansyah; Rizal Tjut Adek; Nunsina
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.v9i1.4021

Abstract

In today's digital era, easy access to information is characterized by the rise of online news portals that present hundreds of articles every day. However, the high volume of news available often makes it difficult for readers to identify the essence of various articles on similar topics. Reading articles one by one becomes a time-consuming activity and risks causing information overload. To address these issues, this research develops an automated system that is able to summarize news based on certain topics by utilizing the Relevance-Augmented Generation (RAG) approach reinforced by the use of Large Language Models (LLMs). The system is built using Python programming language and integrated with Flask framework as a web interface. Data collection is done through a scraping process from the Detiknews portal using a special API. The articles obtained were then analyzed using natural language processing (NLP) techniques, including evaluation of sentence length, sentence position in the article, as well as the frequency of keyword occurrence to determine the most relevant sentences. The initial summary generated is further refined with the help of the LLMs model through the Groq API. The implementation results show that the system is able to present information-dense, accurate, and efficient summaries. The summary makes it easy for users to get the gist of the news quickly without losing the main context. Thus, this system provides a solution to the challenges in online news information processing while increasing the ease of access to information for readers.
PENGEMBANGAN APLIKASI KLASIFIKASI INDIVIDU DENGAN GANGGUAN SPEKTRUM AUTISME BERDASARKAN DSM-5 MENGGUNAKAN PENDEKATAN NAIVE BAYES Ihsan Ihsan; Lathifah Alfat; Riny Nurhajati
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/mnx97052

Abstract

The neurological disorder known as autism spectrum disorder (ASD) has an impact on a person's behavior, social communication, and interest patterns. Both repeated habits and communicative skills are lacking in this developmental condition. The World Health Organization (WHO) estimates that one out of every 100 youngsters worldwide has ASD. The Indonesian Ministry of Health in 2021 showed data on the increasing number of children with autism, which reached around 2.4 million with cases reaching 500 children every year. The use of machine learning can help classify and predict ASD based on health parameters. Using the Naive Bayes algorithm and Diagnostic and Statistical Manual of Mental Disorders 5 (DSM-5) data, this study attempts to create a classification application for people with ASD and assess how the model performs in grouping people with ASD. The results showed that the classification model developed produced optimal performance with an accuracy value reaching 95% while the highest precision, recall and F1-score values reached 100%. Evaluation using the macro average metric resulted in a precision value of 94%, recall of 87%, and f1-score of 90%. The weighted average metric produces positive precision, recall, and F1-score values of 95%. The developed model is integrated into a web-based application that features real time early screening and storage of user prediction results. The development of this application is expected to facilitate early screening so as to help determine effective interventions for individuals with autism spectrum disorders, thus making a positive contribution to the treatment of this disorder in daily practice.
ANALISIS PERBANDINGAN ALGORITMA APRIORI DAN FP-GROWTH DALAM MENENTUKAN POLA PEMBELIAN KONSUMEN TOKO BANGUNAN Muhimmatul Arofah; Mohammad Idhom; Kartika Maulida Hindrayani
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/v9bepx08

Abstract

Micro, Small, and Medium Enterprises (MSMEs) are vital to Indonesia's economy but often face challenges in inventory control and understanding consumer behavior. This study aims to compare the performance of the Apriori and FP-Growth algorithms in identifying consumer purchasing patterns from 7,778 transaction records at UD. Kurnia, a building material store, between August 2023 and July 2024. Unlike previous research that relied only on support and confidence metrics, this study applies the lift metric, which measures the strength of item associations, to minimize misleading rules. The algorithms were tested under 15 combinations of minimum support and lift threshold values. Results show that both algorithms generate the same association rules, but Apriori is significantly faster. At a minimum support of 0.0005 and a lift threshold of 1.5, Apriori completes processing in 3.23 seconds, while FP-Growth takes 21.81 seconds. With these findings, store owners can make more precise inventory decisions and implement data-driven cross-selling strategies, such as offering semen gresik when colt pasir is purchased.
SISTEM REKOMENDASI PEMILIHAN DOSEN PEMBIMBING SKRIPSI MENGGUNAKAN METODE SMART DI UNIVERSITAS BANTEN JAYA Raden Kania; Reni Febriani; Tifani Intan Solihati; Nur Hidayanti; Achmad Radimas
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.v9i1.4034

Abstract

The appointment of a lecturer tutoring the thesis at Banten Jaya University specifically has a computer science faculty. This faculty has five curricula. In the study program of Computer Engineering, the selection of a lecturer tutor for the thesis by the head of the study program, which in the process is done by determining directly by the head of the study program because the appointment of the tutor is determined through the meeting, because of the number of students who follow the theses then head of the study program takes a long enough time to choose the lecturer to each student. Then a decision-making system is needed so that students can identify tutoring lecturers by calculating based on criteria such as competence, linearity, functional post, and quota of lecturers. This study aims to build a decision support system that can give recommendations and determine the tutor of the thesis. Based on that, the research has developed a system with decision-making techniques using the Simple Multi-Attribute Rating Technique (SMART) method. In contrast, the system development aids use the waterfall model and the Unified Modeling Language (UML) as system design aids. The result is a decision-support system for the recommendation of lecturer tutoring a thesis based on the Website, from the results of the tests carried out removed the system, the system can give the recommendation lecturer to the student based on calculations made using the SMART method, the system also provides features for the selection of lecturers tutors in the thesis.
FACE RECOGNITION USING MOBILENETV2 AS A SUPPORT FOR DIGITAL PAYMENT APPLICATION USER AUTHENTICATION Muhammad Ilfanza Mustafavi; Mohammad Nasucha
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.v9i1.4037

Abstract

The increasing use of digital payments potentially elevates the risk of personal data theft and unauthorized access to applications. To mitigate this, biometric-based authentication, such as facial recognition, can be implemented. This study aims to utilize facial recognition as user authentication within an application. The facial recognition is developed using MobileNetV2. This research encompasses data collection, pre-processing, data splitting, data augmentation, model training, model evaluation, and application development. The total facial image data collected was 100 images from 5 classes with an image size of 160 x 160 pixels in .jpg format, sourced from direct photography using a smartphone camera (12MP resolution) under controlled indoor lighting conditions with consistent distance of approximately 50 cm from the subject. The model was successfully implemented with an accuracy of 85%. The model achieved successful implementation with 85% accuracy for real-time facial authentication in digital payment applications.

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