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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) JSAI (Journal Scientific and Applied Informatics) TIN: TERAPAN INFORMATIKA NUSANTARA Bulletin of Computer Science Research Jurnal Penyuuhan dan Pemberdayaan Masyarakat (JPPM) Jurnal Algoritma Jurnal Pengabdian Masyarakat Sains dan Teknologi Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Jurnal Penelitian Sistem Informasi Jurnal Satya Informatika Seminar Nasional Inovasi Teknologi Kocenin Jurnal Pengabdian Kepada Masyarakat BATIK: Jurnal Pengembangan dan Pengabdian Masyarakat Multikultural Journal of Applied Research In Computer Science and Information Systems Green Engineering: Journal of Engineering and Applied Science Jurnal Pengabdian Masyarakat Terapan Jurnal Informatika Dan Tekonologi Komputer Proceeding of The International Conference on Electrical Engineering and Informatics Integrated System and Management Technology Software Engineering in Computing Systems
Safrizal Safrizal
Universitas Pembangunan Jaya
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Analisa Perbandingan Metode SARIMAX dan Prophet Dalam Prediksi Kebutuhan Beras Aditya, Fadhila; Safrizal, Safrizal
TIN: Terapan Informatika Nusantara Vol 6 No 6 (2025): November 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i6.8599

Abstract

Accurate forecasting of rice demand is essential to maintain the balance between supply and consumption at the regional level. However, seasonal fluctuations and dynamic population growth often cause mismatches between rice availability and demand. This study addresses these issues by comparing two time-series forecasting methods, the Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) and Prophet, in predicting rice demand in Central Java Province, Indonesia. The comparison was conducted because SARIMAX effectively explains the influence of exogenous variables such as population, while Prophet offers greater flexibility in capturing nonlinear trends and seasonal patterns. The study used secondary data from the Central Bureau of Statistics (BPS) covering the period from January 2021 to December 2023. Model accuracy was evaluated using MAE, RMSE, MAPE, and sMAPE metrics. The results show that Prophet achieved a MAPE of 4.72%, outperforming SARIMAX at 5.49%, categorized as “highly accurate.” Prophet was more adaptive to short-term variations, whereas SARIMAX provided stronger interpretability of causal factors.
An Artificial Intelligence Based Recommendation Model for Personalizing Students' Learning Interest Paths at Universities Safrizal Safrizal; Chaerul Anwar; Augury El Rayeb
Proceeding of the International Conference on Electrical Engineering and Informatics Vol. 1 No. 2 (2024): July : Proceeding of the International Conference on Electrical Engineering and
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/iceei.v1i2.28

Abstract

This study explores the integration of artificial intelligence (AI) in education, particularly in supporting personalized learning. AI presents new opportunities through adaptive learning platforms, virtual tutors, and intelligent assessment systems that have the potential to revolutionize teaching and learning methods. By conducting in-depth data analysis, AI can identify student performance patterns and provide tailored recommendations, enabling educators to deliver more targeted interventions. Furthermore, personalized learning plays a crucial role in enhancing student motivation and engagement by customizing learning experiences to meet individual needs and learning styles. This study aims to implement personalized learning strategies in educational settings and offers insights into best practices for their integration. It also examines their impact on student engagement and academic achievement. The findings highlight the importance of personalized learning in fostering an inclusive and effective educational environment. By leveraging AI, educators can optimize learning, empower students, and address achievement gaps. This study provides practical recommendations for educators and policymakers to implement AI-based learning strategies effectively.
Analisis Sentimen Ulasan Aplikasi Gojek Menggunakan Metode Random Forest dan K-Means Clustering Nabeel Fazle Mawla Buntaran; Safrizal Safrizal
JURNAL PENELITIAN SISTEM INFORMASI (JPSI) Vol. 4 No. 2 (2026): Mei: JURNAL PENELITIAN SISTEM INFORMASI
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jpsi.v4i2.3792

Abstract

This study aims to examine user opinion tendencies toward Gojek services by integrating Random Forest and K-Means Clustering approaches. The dataset consists of 15,000 user reviews collected throughout 2025 using web scraping techniques. The initial stage focuses on data preprocessing, including text cleaning, case normalization, tokenization, removal of non-informative stop words, and lemmatization to restore words to their base forms. Subsequently, sentiment labels are assigned using a lexicon-based approach. The next phase involves classification modeling through Random Forest to identify sentiment tendencies, while K-Means Clustering is employed to uncover latent patterns within the opinion data. The findings indicate that the Random Forest model achieves an accuracy level of 0.878, demonstrating strong performance in distinguishing positive and negative sentiments, as reflected by f1-scores of 0.932 and 0.818, respectively. However, the model shows limitations in consistently identifying neutral sentiment. In contrast, the implementation of K-Means Clustering successfully categorizes the data into three primary clusters, providing a more structured representation of user opinion characteristics. Overall, these results offer empirical insights that can serve as a strategic reference for enhancing the quality of Gojek’s service delivery.
White Box Testing with Path Testing on the Web-Based Population and Civil Registration Service (Dukcapil) Submission Status Notification Module in Kuningan Barat Subdistrict Dimas Abimanyu Panji; safrizal safrizal
Journal of Applied Research In Computer Science and Information Systems Vol. 3 No. 1 (2025): Juni 2025
Publisher : PT. BERBAGI TEKNOLOGI SEMESTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61098/jarcis.v3i1.257

Abstract

Software testing is a stage in ensuring the quality and reliability of the system before it is implemented operationally. This study aims to evaluate the accuracy of the program logic flow in the web-based DUKCAPIL service submission status notification module in Kuningan Barat Village using the white box testing method with a path testing approach. This method is to analyze the internal structure of the program code to ensure that all logic paths have been thoroughly tested. The testing process begins with the identification of the path basis using the control flow (Control Flow Graph), calculation of cyclomatic complexity, and determination of the test path set. The test results show that all logic paths in the notification module have been passed and no logical errors were found in the program flow. Thus, this module is declared to have met the eligibility criteria in terms of internal logic. This study is expected to be a reference in improving the quality of testing web-based public service information systems, especially in terms of the reliability of population administration service notifications.
Educational Program on Converting Plastic Waste into Alternative Fuel Using Pyrolysis and Triboelectric Nanogenerators (TENG) for Community Leaders in Kasomalang Kulon Tourism Village, Subang Regency Hayadi Hamuda; Lukman Medriavin Silalahi; Safrizal Safrizal; Cahyono Budy Santoso; Yunus Widjaja; Chaerul Anwar; Teddy Mohamad Darajat; Listiana Satiawati; Sumihar M.L. Tobing; Aditiameri Aditiameri
Jurnal Pengabdian Masyarakat Sains dan Teknologi Vol. 5 No. 2 (2026): Juni: Jurnal Pengabdian Masyarakat Sains dan Teknologi
Publisher : Fakultas Teknik Universitas Cenderawasih

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58169/jpmsaintek.v5i2.1066

Abstract

This Community Service (CS) initiative was executed collaboratively by faculty members from five universities—Universitas Pamulang, Universitas Presiden, Universitas Trisakti, Universitas Pembangunan Jaya, and Universitas Borobudur—in Kasomalang Kulon Tourism Village, Subang Regency, West Java, on April 17, 2026. This initiative aimed to instruct village leaders, as prospective catalysts for change in waste management and renewable energy, on the technique for converting plastic trash into pyrolysis-derived alternative fuel and the Triboelectric Nanogenerator (TENG) energy harvesting technology. This activity addresses the critical issues of plastic waste pollution and the exhaustion of fossil fuel reserves. Annually, almost 8 million tonnes of plastic waste infiltrate the oceans, yet merely 9% of all plastic ever manufactured is effectively recycled. The collaborative effort encompassed Electrical Engineering, Computer Systems, Petroleum Engineering, Information Systems, Product Design, and Agrotechnology. The instructional techniques employed comprised lectures and conversations. The activity's results indicated that participants acquired a thorough comprehension of pyrolysis mechanisms, the principles of TENG energy conversion, and its possible applications at the village level. Despite the absence of direct implementation, the cadres shown significant motivation to facilitate technology adoption inside their communities. This PKM model demonstrates that interdisciplinary collaboration across universities is effective in providing innovative technological education to rural populations.
An Integrated Framework of Enterprise Architecture and Artificial Intelligence for Optimizing Strategic Decision Making in Digital Service Oriented Organizations Imeldawaty Gultom; Dedi Candro Parulian Sinaga; Safrizal Safrizal
Integrated System and Management Technology Vol. 1 No. 1 (2026): January: Integrated System and Management Technology
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/ismat.v1i1.5

Abstract

This research explores the integration of Enterprise Architecture (EA) and Artificial Intelligence (AI) to optimize strategic decision-making in digital service-oriented organizations. These organizations often face challenges such as fragmented decision-making due to disconnected IT systems and limited data-driven insights. The objective of the study is to develop an integrated framework that combines EA and AI to enhance decision-making accuracy, operational efficiency, and strategic alignment. The study employs design science research methodology, involving the development of the framework, expert validation, and testing in simulated organizational scenarios. The findings reveal that the integrated framework improves decision-making by providing real-time, data-driven insights, predictive analytics, and better alignment with organizational goals. AI's role in analyzing large datasets and generating actionable insights allows decision-makers to anticipate future trends and make more informed decisions. The framework significantly outperforms traditional EA approaches, particularly in terms of predictive decision support and adaptive intelligence. The study concludes that the integration of EA and AI provides a robust solution for organizations looking to improve strategic decision-making, enhance operational efficiency, and stay competitive in dynamic business environments.
Framework for Integrating Continuous Integration and Continuous Deployment (CI or CD) with Automated Security Testing to Improve Software Dependability Syaiful Anwar; Irwanto Irwanto; Safrizal Safrizal
Software Engineering in Computing Systems Vol. 1 No. 1 (2026): February: Software Engineering in Computing Systems
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/secons.v1i1.47

Abstract

The increasing demand for rapid software delivery has led to the widespread adoption of Continuous Integration (CI) and Continuous Deployment (CD) pipelines. These pipelines automate the processes of code integration, testing, and deployment, significantly improving the speed and reliability of software development. However, traditional CI or CD pipelines often overlook security testing, leading to vulnerabilities in the deployed software. To address this gap, this study proposes an integrated framework that embeds automated security testing within the CI or CD process. The framework incorporates security testing tools such as Static Application Security Testing (SAST), Dynamic Application Security Testing (DAST), and Vulnerability Assessment and Penetration Testing (VAPT) to ensure continuous security checks throughout the development lifecycle. The experimental results show that the proposed framework enhances early vulnerability detection, with detection rates increasing from 30% to 70%. Additionally, the framework reduces deployment failures from 50% to 20%, demonstrating its effectiveness in improving software dependability. While the integration of automated security testing adds a slight 5% increase in pipeline execution time, this minimal impact does not significantly affect the overall speed of the pipeline. The proposed approach successfully balances security and efficiency, ensuring that software is both secure and delivered at high speed. This research highlights the importance of integrating security into CI or CD pipelines and demonstrates that it is possible to achieve high security without sacrificing the speed of software development. The study also discusses the practical implications for software development teams and suggests areas for future research, including the integration of advanced AI-driven security testing tools and the expansion of the framework's applicability across different software projects.
Perbandingan K-Means, Hierarchical Clustering Dan K-Medoids Untuk Segmentasi Pasar Berdasarkan Evaluasi Silhouette Score Denny Ganjar Purnama; Safrizal; Cahyono Budy Santoso
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 2 (2026): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i2.10849

Abstract

This study compares the performance of K-Means, Agglomerative Hierarchical Clustering, and K-Medoids algorithms for market segmentation using PT XYZ sales data. The dataset consists of the Quantity and Expected Revenue attributes and was processed through data cleaning, currency-to-numeric conversion, invalid data removal, logarithmic transformation, and standardization, resulting in 923 valid records. Clustering was performed using three clusters, representing Low Value, Mid Value, and High Value customer segments. Performance was evaluated using the Silhouette Score, where K-Means achieved 0.4805, Agglomerative Hierarchical Clustering 0.4808, and K-Medoids 0.4840. Although the performance differences among the algorithms were relatively small, K-Medoids achieved the highest score and was therefore selected as the final model. The resulting segmentation consisted of 188 Low Value customers (20.37%), 469 Mid Value customers (50.81%), and 266 High Value customers (28.82%). These findings indicate that K-Medoids provides the best clustering quality while offering greater interpretability through medoid-based cluster centers representing actual data objects. The proposed segmentation can support companies in developing differentiated marketing strategies for low-, medium-, and high-value customer segments.
Penerapan Teknik Clustering untuk Analisis Pemerataan Angkutan Umum di Kabupaten Bogor Safrizal Safrizal; Emir Muhammad Al-Fariq
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 5 No. 3 (2025): November : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v5i3.8417

Abstract

This research analyzes the distribution of public transport services from AKDP and AKDK units in 40 sub-districts in Bogor Regency, an area with rapid population growth that faces congestion due to dependence on private vehicles. The main problem is that service gaps have not been mapped quantitatively, so the aim of this research is to apply clustering techniques to identify underserved areas and provide an objective basis for data-based policy recommendations. The proposed method is a quantitative approach using the K-Means Clustering algorithm on secondary datasets from the Department of Transportation with three main features: number of licensed fleets, number of unlicensed fleets, and total fleet. The main findings succeeded in classifying sub-districts into three clusters, Cluster 0 (established services), Cluster 1 (static growth), and Cluster 2 (significant service gaps dominated by informal fleets). In conclusion, this research proves the existence of significant service disparities, and shows that the clustering method is effective in mapping priority zones for more equitable transportation policy interventions.
Penerapan Algoritma K-Means Untuk Menganalisis Berdasarkan Jenis Dan Penyebab Kematian Di Jawa Barat Safrizal Safrizal; Variant Prakasa Wibowo
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 5 No. 2 (2025): Juli : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v5i2.7148

Abstract

West Java Province is one of the regions in Indonesia with fluctuating mortality rates each year. The high number of deaths in various areas, with diverse characteristics of death types and causes, encourages the need for in-depth analysis to map the pattern of mortality cases. This study aims to analyze mortality data in West Java Province based on the type of death, cause of death, and number of cases using the K-Means Clustering method. The data used in this study is secondary data obtained from the official Open Data Jawa Barat portal for the period 2019 to 2023, with a total of 506 mortality records that have undergone pre-processing and data transformation stages. The analysis process was carried out through clustering using the K-Means algorithm, with the optimal number of clusters determined by the Elbow Method, and the clustering quality evaluated using the Silhouette Score. The results showed that the mortality data could be grouped into three main clusters, each with different characteristics in terms of the number of deaths, types, and causes. Cluster 0 consists of data with a high number of deaths, mostly occurring in urban areas. Cluster 1 contains data with moderate numbers and a variety of causes, while Cluster 2 represents areas with low mortality rates spread across several districts. The obtained Silhouette Score value of 0.52 indicates that the quality of cluster separation is quite good. In addition to clustering results, data visualization showed that Dengue Fever has been the dominant cause of death in the last three years, with Cianjur Regency recorded as the region with the highest mortality cases. This study is expected to serve as a reference for formulating public health policies and mortality prevention strategies in West Java Province.
Co-Authors Aditiameri Aditiameri Aditya, Fadhila Agung Priambodo Andy Julianto Augury El Rayeb Cahyono Budy Santoso Cahyono Budy Santoso Chaerul Anwar Chaerul Anwar Chaerul Anwar Chrisamudra, Rosi Windi Dedi Candro Parulian Sinaga Denny Ganjar Purnama Denny Ganjar Purnama Difa Pratama Hakim Dimas Abimanyu Panji Emir Muhammad Al-Fariq Erick Fernando Faizah Syihab Faizal Zuli Hamuda, Hayadi Hudri Ritonga, Adrian Marsa Imeldawaty Gultom Irwanto Irwanto Johannes Hamonangan Siregar Julianto, Ribut Junaidi Junaidi Kiyota Hashimoto Lathifah Alfat, Lathifah Listiana Satiawati Lukman Medriavin Silalahi Lukman Medriavin Silalahi Nabeel Fazle Mawla Buntaran Nizirwan Anwar Panji Wijaya Komara Prabandini Kartika Sulaiman Savero Sagi Sumadikarta , Istiqomah Sumihar M.L. Tobing Syaiful Anwar T. Adi Kurniawan Teddy Mohamad Darajat Turkhamun Adi Kurniawan Variant Prakasa Wibowo Warnars, Harco Leslie Hendric Spits Yuanita Sinatrya Yunus Widjaja