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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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PREDICTING REVENUE OF SHARIA BANKING TRANSACTIONS USING RNN, LSTM, GRU, DECISION TREE, AND QSPM (CASE STUDY: PT BANK TBV SYARIAH) Septian Fakhrudin Arianto; Hasanul Fahmi
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 7 No. 2 (2024): 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.v7i2.3467

Abstract

The banking business will continue to grow significantly along with the increase in the number of transactions carried out by customers through the channels provided by the bank. The variety of products and features offered by PT Bank TBV Syariah to customers means that resources are not optimal. Hence, the bank's revenue growth target still needs to be achieved. This research aims to predict transactions that can affect bank revenues by using transaction data sources for the period January 2022 to February 2024 and which products and features need to be optimized so that it is hoped that banks can run their business appropriately and according to targets. The methods in this research are the RNN, LSTM, GRU, and Decision Tree methods. To enrich information, this research adds QSPM-based strategy analysis using SWOT that the company previously defined. The expected results are to prove the effectiveness of the model used in predicting PT Bank TBV Syariah transaction data to produce MAE, MSE, and RMSE with the lowest values​​, as well as recommendations that PT Bank TBV Syariah must carry out to increase revenue. This research is expected to provide accurate and effective predictions for projecting PT Bank TBV Syariah transaction data, support strategic decision-making, and produce recommendations for significantly increasing bank income.
PEMBUATAN SISTEM PEMANTAUAN PH DAN PPM AIR PADA HIDROPONIK BERBASIS IoT Agus Hermanto; Tony Ahmad Husein Al Mubarok; Agung Kridoyono
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 1 (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.v8i1.3446

Abstract

The reduction in agricultural land due to being converted into housing and urban areas means that the amount of food production is decreasing. Based on the problems above, the author offers an innovation to overcome the problem of food security through "Smart Farming". This innovation can be implemented by the community using the concept of integrated agriculture with fisheries which is easy to apply on minimal land which has not been utilized properly. However, currently There is currently no system that can monitor the pH and PPM levels of water automatically and easily. This has an impact on the poor quality of hydroponic plant production so that it cannot be sold to consumers and causes losses for this business Internet of Things (IoT) and website, this smart farming innovation was developed as a system for monitoring the condition of pH and PPM levels of water in hydroponic planting media. The development of a website-based pH and PPM monitoring system offers an innovative approach in utilizing technology to overcome the challenges of monitoring water quality effectively. By utilizing the advantages of information technology and the internet, this system can become an important solution in supporting efforts to protect the environment and sustain water resources in the future.
PENGEMBANGAN ALUMNI RELATION MANAGEMENT FTI UKDW BERBASIS WEBSITE SEBAGAI MEDIA MEMPERERAT RELASI DENGAN ALUMNI Ivan Pradipta Gunawan; Restyandito Restyandito; Rosa Delima
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 1 (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.v8i1.3451

Abstract

Universities face the challenge of a lack of alumni engagement, with many losing contact after graduation. This results in difficulties in leveraging alumni potential. Insufficient communication leads alumni to feel that their relationship with the university is limited to their time as students. The low response rate to Tracer Studies, which track alumni careers and gather feedback, highlights this issue. Data from various universities indicate low response rates, primarily due to weak relationships and alumni's lack of awareness about the importance of Tracer Studies. Therefore, the development of an Alumni Relation Management system is necessary. The development of a web-based Alumni Relation system used the prototyping method. Evaluation was conducted using Heuristic Evaluation with experts, followed by system implementation through email campaigns to enhance alumni-university engagement. These steps aim to address alumni engagement issues and improve Tracer Study response rates. System testing was performed using black box testing and usability testing. The results indicated that the system functions well in both functional and non-functional aspects. Based on these findings, the system is ready for use and is expected to enhance alumni engagement and the effectiveness of Tracer Studies.
EVALUASI KUALITAS FITUR LAYANAN APLIKASI CUSTOMER INFORMATION SYSTEM (CIS) PDAM SURYA SEMBADA KOTA SURABAYA MENGGUNAKAN METODE E-SERVQUAL Adelia Desi Agnesita; Hawwin Mardhiana; Aris Kusumawati
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 1 (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.v8i1.3460

Abstract

PDAM Surya Sembada Surabaya has developed the Customer Information System (CIS) application since 2013 to facilitate bill payment transactions and complaints about water problems and so on. Despite more than 100,000 downloads, some users complained about the service, including problems recording water meters and failures when registering. Therefore, it is necessary to evaluate the quality of service on the CIS PDAM Surya Sembada Kota Surabaya application using E-Service Quality (E-Servqual) to measure the level of user satisfaction. This method is a measurement dimension to determine service quality which focuses more on electronic services. E-Servqual generally provides how well a company is able to meet customer needs through the use of electronic facilities. This evaluation is needed to find areas of improvement to increase user satisfaction. The aim of this research is to identify factors that influence user satisfaction of CIS PDAM Surya Sembada Surabaya City and provide recommendations for service improvements. This research uses quantitative methods by collecting data through surveys and applying purposive sampling techniques based on primary data obtained through questionnaires given to respondents. -From the analysis it was found that the dimensions of efficiency, system availability, compliance, privacy, responsiveness and compensation did not have a positive and significant impact on user satisfaction. However, the contact dimension has a patch coefficient value of 0.764, a t-statistic value of 9.714 (>1.98) and a p-value of 0.000 (<0.05) indicating that the contact variable has a positive and significant effect on user satisfaction. The ability to contact PDAM service centers via the CIS application has a positive and significant impact, demonstrating the importance of easy and effective access to customer service to increase user satisfaction.
PENGELOMPOKAN TRANSAKSI KARTU DEBIT PERBANKAN MENGGUNAKAN ALGORITMA K-MEANS Iwan Irawan; Reza Rahman; Arief Wibowo
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 1 (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.v8i1.3558

Abstract

One of bank customers' most widely used non-cash payment methods is making payments to merchants using debit cards. The data generated from these transactions can be utilized effectively by banks. This study analyzes customer spending habits through debit card transactions, employing a data mining technique called K-means clustering. By identifying patterns in customer transactions, the research aims to assist business units in developing targeted product strategies. The analysis determined that four clusters were optimal, resulting in a tightly grouped dataset with an average distance of 5.764 from the respective cluster centers. Grouping nominal transactions based on the date and time of the transaction can provide valuable insights for bank management when considering customer fund allocation.
PERBANDINGAN METODE NAÏVE BAYES DAN SVM UNTUK SENTIMEN ANALISIS MASYARAKAT TERHADAP SERANGAN RANSOMWARE PADA DATA KIP-K Nabil Safiq Ramadan; Dedi Darwis
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 1 (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.v8i1.3621

Abstract

This research examines ransomware attacks on KIP-K data by analyzing the opinions of Social Media X users, using the naïve bayes classifier (NBC) and support vector machine (SVM) methods. The rapid development of technology not only brings great benefits but also increases the risk of digital attacks by certain parties. One example is a ransomware attack that caused a KIP-K data leak. In this study, sentiment analysis was applied to identify public opinions or responses obtained from Social Media X, with the help of python programming and google colab. Of the total 2,648 raw data collected, pre-processing was carried out resulting in 1,738 cleaned data. The study compared two methods, namely Naïve Bayes and Support Vector Machine, to determine what method is more effective in analyzing public sentiment related to ransomware attacks on KIP-K data. The focus of this study is to understand the percentage of Social Media X users' comments and responses related to the KIP-K ransomware taken from media sosial X. The stages of sentiment analysis in this study include crawling, labeling, preprocessing, method classification, and visualization. Before the classification process was carried out, the data was divided into two parts, namely 30% for test data and 70% for training data. Data labeling resulted in 1,313 negative data, 957 positive data and 377 neutral data. The classification results show that the NBC method has an accuracy of 70%, while the SVsM achieves an accuracy of 88%. Based on these results, SVM is proven to be superior in data analysis compared to NBC, especially for big data.
ANALISIS SENTIMEN MASYARAKAT TERHADAP KASUS JUDI ONLINE MENGGUNAKAN DATA DARI MEDIA SOSIAL X PENDEKATAN NAIVE BAYES DAN SVM M Febrian As Shidiq; Debby Alita
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 1 (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.v8i1.3624

Abstract

Research conducted by analyzing public sentiment related to online gambling cases using datasets from x social media using the naïve bayes method approach and support vector machine (SVM). The analysis phase starts with data gathering or crawling, followed by data labeling, data preprocessing, and ultimately method categorization. The dataset comprises 2,866 tweets, with 1,436 classified as positive (50.12%) and 1,429 as negative (49.88%). The data before to the classification process is partitioned into training data and testing data, including 70% training data and 30% testing data. The analysis with the SVM approach yielded a classification accuracy of 83%, whereas the naïve Bayes method achieved just 79%. Upon completion of the method classification process, the subsequent phase involves visualization and assessment. During the visualization step, bar plots, word clouds, and word frequencies derived from sentiment analysis calculations are shown, alongside a visualization of words from the dataset. The investigation indicates that the SVM approach outperforms Naive Bayes in sentiment classification. The benefit of SVM resides in its capability to manage data with elevated limits and accuracy, enhancing its efficiency in discerning positive and negative thoughts. The findings of this study demonstrate that SVM is better appropriate for data exhibiting complicated distributions, whereas the Naive Bayes approach yields suboptimal results. Thus, SVM can be proposed as a more appropriate and reliable approach for similar sentiment analysis in the future.
PENGEMBANGAN PROTOTIPE NAVIGASI AUGMENTED REALITY DENGAN FITUR PENGENALAN SUARA MENGGUNAKAN GDLC STUDI KASUS: DI LINGKUNGAN PENDIDIKAN Khadijah Khadijah; Nur Choiriyati; Meita Sekar Rahayu
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 1 (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.v8i1.3654

Abstract

The rapid evolution of digital technology is transforming educational practices, enhancing accessibility and interactivity through innovations like augmented reality (AR) and artificial intelligence (AI). This study aims to develop the PDBI Tour application, an AR-based navigation tool integrated with speech recognition, to improve campus accessibility for new students and promote Politeknik Digital Boash Indonesia, a vocational education in Bogor Regency. A Game Development Life Cycle (GDLC) methodology was adopted, comprising initiation, pre-production, production, testing, beta, and release phases. Data were collected through interviews with promotional teams and prospective students, and prototype design was created using Figma, while Unity was used for production with AR Foundation and Whisper API. Functional testing (black box) confirmed reliable navigation features, and usability testing yielded a 65% satisfaction rate from 25 respondents representation of students in PDBI aged 18-20 years who had tried the prototype, highlighting effectiveness in usability, playability, and accessibility. These findings indicate that the PDBI Tour application prototype meets user needs and offers potential as an immersive navigation and promotional tool. This research demonstrates the GDLC approach’s efficacy in enhancing application development through iterative improvements, validating AR navigation as a powerful tool for interactive and accessible campus experiences.
PENGARUH KOMPOSISI SPLIT DATA PADA AKURASI KLASIFIKASI PENDERITA DIABETES MENGGUNAKAN ALGORITMA MACHINE LEARNING Febby Refindha Aftha Harianto; Zakki Alawi; Ita Aristia Sa’ida
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 1 (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.v8i1.3663

Abstract

The increasing number of people with diabetes is an international health problem. To prevent diabetic complications, early diagnosis and accurate classification are essential. This study looks at how the composition of split data affects the classification performance of diabetics with machine learning algorithms such as Random Forest, Naive Bayes, and Support Vector Machine (SVM). The research data is taken from Bojonegoro Regency Hospital, which consists of 128 samples that have 10 main features. To ensure the data is ready for use, the research method goes through a preprocessing stage. Next, the data was divided into training and testing data with a ratio of 90:10, 80:20, 70:30, 60:40, and 50:50 respectively. Using confusion matrix, the algorithm is assessed for accuracy, precision, recall, and F1 score. In this study we focus on the accuracy values obtained and the results show that the proportion of data sharing affects the performance of the algorithm. Random Forest achieved 100% accuracy in some scenarios. This algorithm also proved to be the most effective in the classification of diabetics. In conclusion, algorithm selection and data split composition are very important for model performance optimization. These results are important for the development of more accurate and efficient Machine Learning-based diagnosis systems. Further research can consider larger datasets and additional algorithms for better results.
IMPLEMENTASI METODE SMOTE DAN RANDOM OVER-SAMPLING PADA ALGORITMA MACHINE LEARNING UNTUK PREDIKSI CUSTOMER CHURN DI SEKTOR PERBANKAN Fannisa Salsabila Pratiwi; Mula Agung Barata; Aprillia Dwi Ardianti
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 1 (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.v8i1.3678

Abstract

The ability to anticipate unsubscribed customers is a challenge in the competitive banking industry, where it is more efficient to retain customers than to attract new ones. The purpose of this study is to improve the effectiveness of churn prediction by overcoming data imbalances using SMOTE (Synthetic Minority Oversampling Technique) and Random Over-sampling. The data set used consists of 10. 000 bank customer data, with 12 important attributes, including churn indicators as targets. The machine learning algorithms used are Random Forest and Neive Bayes, evaluated based on accuracy, precision, recall, and F1 scores. The results of the experiment showed that the highest accuracy of 87.13% could be achieved with the Random Forest algorithm without using the oversampling method, but its effectiveness in detecting churn customers was slightly limited. The use of SMOTE and Random Over-sampling methods has improved the model's performance in identifying churn patterns, although it has led to a decrease in accuracy to 86.20% for Random Over-sampling and 81.47% for SMOTE. Nevertheless, the Neive Bayes algorithm showed the best accuracy rate of 79.20% without oversampling, although it was still slightly lacking in optimal churn handling. The study underscores the importance of using oversampling methods to improve prediction balance in minority classes, which is often overlooked in conventional models. It is hoped that the results of this research can be used as a guide in improving strategies to maintain customer trust that are more up-to-date and efficient.

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