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JITK (Jurnal Ilmu Pengetahuan dan Komputer)
Published by STMIK Nusa Mandiri
ISSN : -     EISSN : 25274864     DOI : -
Core Subject : Science,
Computer Science & IT
Kegiatan menonton film merupakan salah satu cara sederhana untuk menghibur diri dari rasa gundah gulana ataupun melepas rasa lelah setelah melakukan aktivitas sehari-hari. Akan tetapi, karena berbagai alasan terkadang seseorang tidak ada waktu untuk menonton film di bioskop. Dengan bantuan media internet, berbagai macam aplikasi nonton film android sangat mudah dicari. Hanya bermodalkan smartphone saja para penonton film dapat streaming berbagai macam jenis film di mana saja dan kapan saja mereka inginkan. Akan tetapi, karena banyaknya pilihan aplikasi nonton film android yang bisa digunakan, terkadang seseorang bingung memilihnya. Untuk itu, diperlukan suatu sistem pendukung keputusan yang dapat digunakan para pengguna sebagai alat bantu pengambilan keputusan untuk memilih dengan berbagai macam kriteria yang ada. Salah satu metode yang digunakan adalah metode Analytical Hierarchy Process (AHP). AHP melakukan perankingan dengan melalui penjumlahan antara vector bobot dengan matrik keputusan dengan tujuan agar hasil yang diberikan lebih baik dalam menentukan alternatif yang akan dipilih. Berdasarkan hasil penelitian yang dilakukan oleh 36 sampel responden didapatkan kriteria konten menjadi prioritas pertama pengguna untuk memilih aplikasi nonton film android dengan nilai bobot sebesar 0,224. Sedangkan Netflix menjadi alternatif dengan prioritas pertama keputusan pengguna dalam memilih aplikasi nonton film android dengan nilai bobot sebesar 0,352.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 465 Documents
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OPTIMIZATION IN ZAKAT MANAGEMENT THROUGH THE DEVELOPMENT OF A CHATBOT-BASED MOBILE APPLICATION Wihatiko, Fajar Delli; Putra, Gustian Rama
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7402

Abstract

The growing awareness of zakat, infaq, and sadaqah (ZIS) in Indonesia calls for intelligent and transparent management systems. This study proposes a chatbot-based mobile application integrated with the SECI knowledge management model to optimize ZIS management and distribution. Using the waterfall software development model, the research includes requirement analysis, system design, chatbot implementation, and validation. The decision-tree-based chatbot enables interactive and personalized guidance for muzakki, while the SECI framework ensures structured knowledge sharing among zakat institutions. Functional and compatibility testing show that the system operates reliably on Android version 10 and above, with intent classification accuracy reaching 92 percent. The findings demonstrate that combining intelligent interaction and structured knowledge management improves transparency, operational efficiency, and institutional learning in digital zakat systems. The proposed framework provides both theoretical and practical contributions to advancing socio-economic management through mobile technology
COMPARATIVE ANALYSIS OF BAGGING AND BOOSTING MODELS IN ENSEMBLE LEARNING FOR GRADUATION PREDICTION Sartika Lina Mulani Sitio; Darmawati; Yuda Samudra
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7579

Abstract

Student graduation prediction is an important aspect in supporting academic decision-making in higher education. However, conventional evaluation approaches have not been able to identify the risk of early graduation delays. This study aims to compare the performance of two ensemble learning approaches, namely Bagging using Random Forest and Boosting using XGBoost, in predicting student graduation. The study used  the Predict Students' Dropout and Academic Success dataset  consisting of 4,424 student data. Both models were trained on the same data and evaluated using the Accuracy, Precision, Recall, F1-Score, and ROC-AUC metrics. The results of the experiment showed that both models had almost equal accuracy, i.e. 82.6% for Random Forest and 82.5% for XGBoost. However, XGBoost showed better performance on Recall (0.878) and F1-Score (0.834), which indicated a higher ability to detect students who actually graduated. Based on these results, this study concludes that XGBoost is more effective than Random Forest in the context of predicting student graduation and is more suitable to be applied to  the Academic Early Warning System in universities
IMPLEMENTATION OF A SMART CONTRACT-BASED E-VOTING SYSTEM FOR COMPETITIONS Tan, Tony; Valentino, Eric; Simanjuntak, Fredian
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7622

Abstract

Traditional voting methods in competitions often face challenges related to transparency and fraud, undermining fairness. This research presents the design and implementation of a hybrid e-voting system built on Ethereum blockchain technology to mitigate these issues. Specifically, this research integrates an off-chain HMAC-SHA256 privacy mechanism with Ethereum’s Proof-of-Stake (PoS) consensus to ensure that voting records remain immutable and publicly auditable, while preserving voter anonymity. A prototype was developed using a decentralized architecture, leveraging smart contracts to automate the entire electoral process from registration to tallying. An evaluation involving 153 participants based on the Technology Acceptance Model (TAM) demonstrated high user acceptance, with scores of 76.6% for Perceived Usefulness, 73.4% for Perceived Ease of Use, and 72.8% for Acceptance of Technology. Although the system demonstrates effectiveness in competitive settings, current testing is limited to small- to medium-scale implementations. This research concludes that the proposed framework provides a secure, transparent, and efficient alternative for competitions, significantly enhancing trust in the election outcomes.
ADAPTIVE AL-QUR’AN MEMORIZATION RECOMMENDATION SYSTEM BASED ON FUZZY LOGIC COGNITIVE MEMORY AND PROFILE MATCHING Dhiya'ulhaq, Afifah Fikriyah; Fauzi, Muhammad Dzulfikar; Safitri, Pima Hani
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.8048

Abstract

Memorizing mutasyabihat verses in the Qur’an is particularly challenging due to similarities in structure, linguistic patterns, and semantic density that place a heavy load on short-term memory. Conventional memorization approaches do not account for individual cognitive differences when dealing with verse complexity. This study proposes an adaptive recommender system based on cognitive modeling to align verse group selection with the user’s memory profile.The system models memory capacity as a multidimensional profile using fuzzy inference derived from three quantitative indicators: continuous memory score, total correct recall, and average response time. This profile is matched with verse group feature vectors through a profile matching approach and a weighted Euclidean distance similarity measure within a Multi-Attribute Decision Making (MADM) framework. Four verse characteristics are considered: thematic (35%), semantic (25%), linguistic (25%), and pattern (15%).An adaptive calibration phase combines 20% of the initial cognitive profile with 80% of actual memorization performance, reflecting the dominance of behavioral evidence over initial assessment. System evaluation employs the Top-N Accuracy method commonly used in recommender systems.Testing with 29 participants resulted in a Top-3 success rate of 66% and an overall Top-N accuracy of 62.07%. These results indicate that cognitive profile–based multidimensional similarity can adaptively match verse complexity to individual memory capacity. This study demonstrates that fuzzy cognitive modeling and profile matching can be effectively implemented in adaptive personalized learning systems to optimize memorization of mutasyabihat verses
CONVERSION OF GRAPHICAL TO NUMERICAL DATA WITH WEB PLOT DIGITIZER IN OIL RESERVE DETERMINATION Yunita, Lia
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7226

Abstract

Old oil fields that are to be reactivated have production data only in graphical form, making it difficult to determine remaining reserves. Web Plot Digitizer helps convert graphical data into numerical data for determining oil reserves using the decline curve method. The use of Web Plot Digitizer reduces numerical errors, which impact decline parameters (qi, Di, b) and time efficiency in reserve determination. The purpose of this study is to apply Web Plot Digitizer to convert graphical production data into numerical data and determine oil reserves using decline curve analysis. The novelty of this research lies in the use of digitized graph data as direct input in Decline Curve Analysis (DCA) analysis for oil reserve estimation. The purpose of this research is to apply Web Plot Digitizer in converting production graph data into numerical data, as well as determining oil reserves using decline curve analysis. This research method uses exponential Decline Curve Analysis (DCA), which is applied to old oil fields, production rate data in the form of graphs is converted into numerical data using Web Plot Digitizer. The digitized numerical data is then made into a semilog graph of production rate versus time, then a trend line is taken for the decline in oil production rate and used in determining oil reserves. The analysis results obtained an initial decline rate (Di) value of 0.041 per month and oil reserves are estimated at 5 million barrels of oil (5 MBO), where oil will be exhausted in January 1985 if no workover is carried out. The results of this analysis provide a solution for old oil fields that only have historical graphs without access to numerical data, so that they can still calculate reserves using Decline Curve

Page 47 of 47 | Total Record : 465

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