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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 481 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
DEEP LEARNING APPROACH FOR RECOGNIZING SUBSIDIZED GAS RECIPIENTS USING CONVOLUTIONAL NEURAL NETWORKS Sidik, Achmad; Ryando, M. Bucci; Julianti, M. Ramaddan; Rifaldi, Agus
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7454

Abstract

Inaccurate targeting in subsidized LPG distribution remains a persistent policy challenge in Indonesia, where manual verification processes are vulnerable to misuse and administrative error. Addressing this gap, the present study develops and evaluates a biometric identity verification system based on Convolutional Neural Networks (CNNs) to improve the accuracy and accountability of subsidy allocation at the point of distribution. Following the CRISP-DM framework, two CNN architectures with fundamentally different design philosophies were compared: ResNet-IR, optimized for representational depth and recognition accuracy, and MobileFaceNet, designed for computational efficiency on resource-constrained hardware. Both models were sourced from the InsightFace framework as pre-trained models and evaluated on a locally acquired dataset of 111 registered subsidy recipients from Pajang Village, Tangerang City. Evaluation across face identification (1:N) and face verification (1:1) tasks reveals that ResNet-IR consistently outperforms MobileFaceNet, achieving an accuracy of 94.7% with a precision, recall, and F1-score of 0.9043, compared to MobileFaceNet’s accuracy of 93.7% and F1-score of 0.8862. The primary contribution of this work is to demonstrate, for the first time in the Indonesian subsidy distribution context, that deep learning-based facial recognition can serve as a viable, deployable mechanism for biometric identity verification in public service programs offering a technically grounded pathway toward more transparent and equitable subsidy targeting.
EVALUATION OF ANN- LEVENBERG MARQUARDT MODELS FOR FAULT DETECTION IN SMART FARMING SYSTEM Wardhani, Luh Kesuma; Buono, Agus; Wahjuni, Sri; Syukur, Muhamad
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7481

Abstract

Sensor readings in open field monitoring systems are influenced by disruptions, degradation, and operational unreliability. These conditions may result in inaccurate data and unreliable system decisions. However, existing studies focus on detection accuracy and rarely examine the trade-off between detection performance and computational efficiency of Artificial Neural Networks trained using the Levenberg–Marquardt algorithm (ANN–LM) in smart farming environments. This study evaluates the fault-detection capability of ANN–LM for soil moisture sensor readings by analyzing both detection performance (accuracy, precision, recall, and F1-score) and computational efficiency (execution time, CPU usage, and memory consumption), thereby addressing the trade-off between performance and efficiency. Baseline data, hypothetical dataset that represent the soil moisture reading from a smart chilli pepper farming system in normal operating conditions, were used to generate fault-injected datasets representing four common faults: drift, bias, spike, and malfunction. The ANN–LM model was evaluated under five fault-detection scenarios with different network architectures. Model performance was evaluated using accuracy, precision, recall, and F1-score, while computational cost was assessed through execution time, CPU usage, and memory usage. The results show that ANN–LM achieves an accuracy of 0.996–0.999, precision of 1.000, recall of 0.987–1.000, and F1-scores of 0.992–1.000 across all scenarios. Simple ANN architectures give accuracy of 0.997 with reduced execution time (33.74 seconds) and lower CPU usage (50.50%) compared to more complex architectures that require 591.88 seconds and 78.40% CPU usage. Therefore, these results indicate point out that ANN–LM is suitable for smart agricultural systems under resource-constrained conditions.
COMPARISON OF CONVOLUTIONAL NEURAL NETWORK ARCHITECTURES EFFICIENTNET-B4 AND MOBILENETV2 IN CATARACT DISEASE DETECTION Puspitasari, Yuanita; Jong, Jek Siang; Prabawati, Andhika Galuh
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7502

Abstract

Cataracts are the leading cause of blindness worldwide, with 94 million cases reported in 2023. Conventional cataract identification relies on visual examination methods that are prone to error due to their subjective nature. This study compares the performance of two Convolutional Neural Network (CNN) architectures, MobileNetV2 and EfficientNet-B4, in detecting cataract images. The dataset used was sourced from Kaggle and consisted of 1,074 normal images and 1,038 cataract images. The stages included preprocessing, augmentation, and the application of transfer learning with weights from ImageNet. The models were evaluated using accuracy, loss, precision, recall, F1-score, error rate, and visual interpretation using Grad-CAM metrics. The results showed that MobileNetV2 achieved 96% accuracy with an error rate of 4.05%, balanced precision, recall, and F1-score of 0.96, and a loss of 0.60. Meanwhile, EfficientNet-B4 achieved an accuracy of 96.5% with an error rate of 3.47%, balanced precision, recall, and F1-score of 0.97, and a lower loss of 0.12. Further evaluation indicates that EfficientNet-B4 has the same error rate on both training and test data (3.47%) with a loss difference of 0.02, suggesting that the model performs well and does not experience overfitting. In MobileNetV2, the difference in error rate between training (3.28%) and test (4.05%) is relatively small (0.77%), indicating that this model also does not exhibit overfitting. Grad-CAM visualization reveals that EfficientNet-B4 focuses more on clinically relevant areas, whereas MobileNetV2 tends to capture global patterns. Thus, EfficientNet-B4 is considered superior in terms of accuracy and generalization, while MobileNetV2 is more computationally efficient
OPTIMIZING CAYENNE PEPPER PRICE FORECASTING USING HYBRID SARIMAX-LSTM MODEL FOR FOOD SECURITY Supriyatna, Adi; Rahmawati, Mari; Rabbani, Burhanudin; Wenang, Asta; Adly, Sulthan
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7917

Abstract

The price volatility of cayenne pepper in traditional markets significantly impacts household purchasing power and regional inflation. While traditional statistical models can capture seasonal patterns, they often fail to model complex non-linear fluctuations driven by external factors such as weather anomalies and national holidays. To address these limitations, this study proposes a hybrid SARIMAX-LSTM model. The Seasonal AutoRegressive Integrated Moving Average with eXogenous variables (SARIMAX) component is utilized to model linear structures, seasonality, and the influence of exogenous variables (temperature, rainfall, and holidays), whereas the Long Short-Term Memory (LSTM) component specifically models the remaining non-linear patterns within the residuals. Daily data comprising chili prices, weather metrics, and holiday schedules were employed to train and test the model using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) as performance metrics. Experimental results demonstrate that the proposed hybrid model significantly outperforms the single SARIMAX baseline model, reducing the RMSE by 26.7% (from 11.09 to 8.13) and MAPE by 28.6% (from 23.45% to 16.74%). This approach not only provides a more accurate and robust decision-support tool for price stability but also contributes to the advancement of artificial intelligence-based hybrid methods in the domain of food security.
COMPARATIVE STUDY OF RESAMPLING TECHNIQUES FOR STUDENT PERFORMANCE PREDICTION USING SMOTE-ENN AND ENSEMBLE LEARNING Eni Heni Hermaliani; Ahmad Zainul Fanani; Heru Agus Santoso; Affandy
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.v11i3.8214

Abstract

This study analyzes the effectiveness of resampling techniques and ensemble learning in addressing class imbalance problems in student performance prediction using the xAPI-Edu-Data dataset from the Kalboard 360 LMS. The class imbalance ratio of 1:1.66 leads to bias in traditional classification models toward the majority class. The study evaluates six resampling methods, including hybrid SMOTE-ENN, combined with nine individual classifiers and three ensemble models (bagging, voting, and stacking). Evaluation was conducted using accuracy, precision, recall, and F1-score with stratified 5-fold cross-validation and hyperparameter optimization through GridSearchCV. The results indicate that the combination of SMOTE-ENN with voting and stacking achieved the best performance of 98.18% across all evaluation metrics and significantly improved minority-class recall, demonstrating its effectiveness for developing early warning systems to identify at-risk students.

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