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Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK)
Published by PT. Lentera Ilmu Publisher
ISSN : 30312698     EISSN : 30312698     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Other
Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) adalah jurnal nasional berbahasa Indonesia yang dikelola oleh Lentera Ilmu Publisher. Jurnal ini memuat hasil penelitian dengan topik-topik penelitian yang berasal dalam cakupan rumpun sistem informasi seperti perancangan sistem informasi, analisis sistem informasi, tata kelola IT, sistem pengambilan keputusan (SPK) dan teknik informatika meliputi rekayasa perangkat lunak, kecerdasan buatan, machine learning serta bidang-bidang lainnya yang terkait ke dalam rumpun ilmu tersebut. Jurnal ini diterbitkan 2 kali dalam 1 tahun yakni pada bulan Februari, dan Agustus dengan periode penerimaan artikel sepanjang tahun. Artikel yang masuk ke jurnal ini akan di-review oleh mitra bestari sebelum diterbitkan. Proses review artikel dilakukan secara double blind review yang mana mitra bestari tidak mengetahui siapa penulis artikel tersebut dan juga sebaliknya penulis tidak mengetahui mitra bestari yang mereview artikel tersebut. Jurnal JAFOTIK merupakan jurnal akses terbuka (open access) sehingga seluruh artikel yang diterbitkan oleh jurnal ini dapat diakses kapan saja dan di mana saja oleh siapa saja tanpa dipungut biaya.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 45 Documents
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An Explainable Machine Learning Approach for Shortest Path Optimization in Urban Transportation Networks Adriel Moses Anson
Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) Vol. 4 No. 2 (2026): JAFOTIK - August
Publisher : Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/jafotik.v4i2.139

Abstract

Urban transportation networks are characterized by complex spatial structures, dynamic traffic conditions, uncertain travel times, and heterogeneous road characteristics. Conventional shortest-path algorithms, such as Dijkstra's algorithm, generally optimize a predetermined edge cost and therefore may not adequately represent rapidly changing traffic conditions. This study proposes an explainable machine learning framework for shortest-path optimization in urban transportation networks by integrating traffic-time prediction, graph-based representation, shortest-path optimization, and explainable artificial intelligence. The transportation network is represented as a directed weighted graph (G=(V,E)), where vertices represent intersections and edges represent road segments. A machine learning model estimates the expected travel time of each road segment using traffic-related features, including historical speed, traffic volume, road length, congestion level, time of day, day of week, and weather-related variables. The predicted travel time is then incorporated into a dynamic edge-weight function and optimized using a shortest-path algorithm. To improve transparency, SHAP-based explanations are employed to quantify the contribution of each input feature to predicted travel time and, consequently, to route selection. The proposed framework is evaluated using conventional distance-based routing, static travel-time routing, machine-learning-based routing, and explainable machine-learning-based routing. Illustrative simulation results indicate that incorporating predicted traffic conditions can reduce estimated travel time compared with static shortest-path routing, while the XAI component provides a transparent interpretation of why particular road segments are selected or avoided. The proposed approach provides a mathematical and interpretable framework for intelligent urban route planning and can support transportation agencies in developing more adaptive, transparent, and trustworthy intelligent transportation systems.  
Analysis of oil palm production patterns using the DBSCAN method based on historical data at PT. Suryabumi Agrolanggeng Agnes Kristina Sipayung; Syahril Rizal
Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) Vol. 4 No. 2 (2026): JAFOTIK - August
Publisher : Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/jafotik.v4i2.141

Abstract

Oil palm is one of the important plantation commodities that plays a significant role in Indonesia’s economy. PT. Suryabumi Agrolanggeng has historical oil palm production data covering production, rainfall, and fertilizer usage variables for the period 2019–2025. The main challenges are production fluctuations and the suboptimal utilization of historical data to identify production patterns and periods with different characteristics. This study aims to apply the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method to group data based on the similarity of their characteristics. The research stages include data collection, preprocessing, standardization using the Z-Score method, parameter determination using a K-Distance Graph, DBSCAN clustering, and evaluation using the Silhouette Score. The results show that the parameters Eps = 0.95 and MinPts = 6 produce three main clusters, with 58 data points grouped into clusters and 26 data points identified as noise. The resulting Silhouette Score of 0.376 indicates a cluster structure with a moderate level of separation. The analysis results are further presented through a web-based dashboard to facilitate the monitoring and interpretation of production patterns. This study demonstrates that DBSCAN can assist in identifying production patterns and data points with uncommon characteristics, thereby supporting data-driven decision-making.
Design and Implementation of a Computer Network to Improve Data Center Accessibility Using Arista Devices and a Failover Method at Terminal 3 of Soekarno-Hatta Airport Elang Nurjaya; Akrom
Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) Vol. 4 No. 2 (2026): JAFOTIK - August
Publisher : Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/jafotik.v4i2.143

Abstract

Data center network accessibility and availability are critical to supporting reliable data services, particularly in environments requiring continuous connectivity. This study aims to design and implement a redundant computer network at Terminal 3 of Soekarno-Hatta International Airport using Arista devices and a failover mechanism. The proposed architecture adopts a Spine-Leaf topology with VXLAN as the data plane, BGP EVPN as the control plane, and MLAG, Anycast VTEP, and ECMP to provide redundancy and multiple active forwarding paths. The implementation was conducted using Arista vEOS 4.31.0F in a PNETLab simulation environment. Network performance was evaluated through three failure scenarios: access-link failure, Leaf device failure, and Spine device failure. The results showed 0% packet loss in all scenarios, with network recovery measured at less than 200 ms. The Leaf failure caused an approximately 20-second HTTP service disruption, while the Spine failure caused a 130-second disruption. Further analysis indicated that the prolonged Spine failure disruption was caused by different BGP timer configurations between the Arista fabric and FortiGate perimeter, rather than by fabric convergence, which occurred within 9 seconds. The proposed architecture successfully improved network redundancy, resilience, and data center accessibility.
E-Commerce Activity Clustering Based on Online Shopping and Income per Capita in Indonesian Cities Tri Prasetyo; Pandu Wiliantoro; Aldi Feridanto; Iqbal Julyansyah; Liza Sania Khulatus Syafaah
Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) Vol. 4 No. 2 (2026): JAFOTIK - August
Publisher : Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/jafotik.v4i2.144

Abstract

The rapid growth of digital technology and internet penetration has contributed to the expansion of e-commerce activity across cities and regencies in Indonesia. However, the intensity of online shopping does not necessarily correspond directly to regional income per capita. This study aims to examine the relationship between online shopping activity and income per capita and to classify Indonesian cities and regencies based on these characteristics using the K-Means Clustering algorithm. This research employs a quantitative approach with a descriptive-exploratory design. Secondary data were collected from statistical and e-commerce sources and processed through data cleaning, transformation, and normalization. The K-Means Clustering algorithm was subsequently applied to group regions according to their levels of online shopping activity and income per capita. The Elbow Method was used to determine the optimal number of clusters. The results identified three clusters with distinct levels of e-commerce activity. Cluster 0 represented regions with the highest level of activity, followed by Cluster 1 and Cluster 2. Several regions demonstrated relatively high levels of e-commerce activity, including Papua, Central Java, West Java, Riau, and DKI Jakarta. These findings provide a regional overview of e-commerce activity and income characteristics that can support digital business strategies and contribute to the development of more inclusive and equitable digital economic policies.
Predicting Timely Graduation of Students with Disabilities at Universitas Pamulang Using Decision Tree and Random Forest Based on Historical Academic Data Muhammad Azis Sularso; Riky Susanto; Juri Pebrianto; Ilham Firmansyah Siddhi
Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) Vol. 4 No. 2 (2026): JAFOTIK - August
Publisher : Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/jafotik.v4i2.145

Abstract

Timely graduation is an important indicator of academic success, particularly for students with disabilities who may require additional academic support. This study aims to predict timely graduation among students with disabilities at Universitas Pamulang using historical academic data and to compare the performance of Decision Tree and Random Forest algorithms. The study employed a quantitative approach and data mining methods using data from 125 students, with 100 records used for training and 25 records for testing. The prediction models were developed using academic variables, including semester GPA, cumulative GPA, total credits earned, study duration, attendance, and disability-related variables. Model performance was evaluated using accuracy, precision, recall, F1-score, confusion matrix, and 5-fold cross-validation. The results showed that Random Forest outperformed Decision Tree in terms of testing accuracy, achieving 88.00% compared with 80.00%. Random Forest also achieved a 5-fold cross-validation accuracy of 97.00% ± 0.0245. Feature importance analysis indicated that IPS_S4, IPS_S3, and AVG_IPS were among the most influential variables. These findings suggest that Random Forest can serve as a supporting tool for the early identification of students who may be at risk of delayed graduation and can help inform data-driven academic interventions.

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