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Analisis Usability Website Universitas Hamzanwadi terhadap Kepuasan Pengguna dengan Menggunakan User Satisfaction Model Amri, Zaenul; Uska, Muhammad Zamroni; Arianti, Baiq Desi Dwi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 2, No 1 (2018): Edumatic : Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar

The purpose of this study is to find out: (1) the level usability of the hamzanwadi university website for user satisfaction by using the user satisfaction model, (2) model produced by Green and Pearson 2010 with 9 variables measurement including: design credibility, content, interactivity, navigability, responsiveness, download delay, perceived usefulness, perceived ease of use, user satisfaction. This type of research is quantitative by using survey method. The population of this study is all students of Faculty of Teacher Training and Education (FKIP) Hamzanwadi University with the numbers of 3724 students and the numbers of samples in this study were 361 students using cluster sampling technique. Data collection techniques in this study using a questionnaire with likert scale. The technique used to analyze the data is descriptive statistics. The result of this study shows that (1) from 9 variable measurement of usability of website of Hamzanwadi University only 4 variables that stated good and able to fulfill user satisfaction there are, design credibility variable, content, and perceived ease of use with mean value = 11, and interactivity with value mean = 7, (2) Â 5 other variables are enough and have not able to meet user satisfaction there are, variable navigationability, responsiveness, download delay, perceived usefulness, and user satisfaction with mean value = 10. So it can be concluded that the overall usability of the website of Hamzanwadi University has not can meet user satisfaction.

Analisis Usability Website Universitas Hamzanwadi terhadap Kepuasan Pengguna dengan Menggunakan User Satisfaction Model Zaenul Amri; Muhammad Zamroni Uska; Baiq Desi Dwi Arianti
Jurnal Pendidikan Informatika (EDUMATIC) Vol 2, No 1 (2018): Edumatic : Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v2i1.842

The purpose of this study is to find out: (1) the level usability of the hamzanwadi university website for user satisfaction by using the user satisfaction model, (2) model produced by Green and Pearson 2010 with 9 variables measurement including: design credibility, content, interactivity, navigability, responsiveness, download delay, perceived usefulness, perceived ease of use, user satisfaction. This type of research is quantitative by using survey method. The population of this study is all students of Faculty of Teacher Training and Education (FKIP) Hamzanwadi University with the numbers of 3724 students and the numbers of samples in this study were 361 students using cluster sampling technique. Data collection techniques in this study using a questionnaire with likert scale. The technique used to analyze the data is descriptive statistics. The result of this study shows that (1) from 9 variable measurement of usability of website of Hamzanwadi University only 4 variables that stated good and able to fulfill user satisfaction there are, design credibility variable, content, and perceived ease of use with mean value = 11, and interactivity with value mean = 7, (2) 5 other variables are enough and have not able to meet user satisfaction there are, variable navigationability, responsiveness, download delay, perceived usefulness, and user satisfaction with mean value = 10. So it can be concluded that the overall usability of the website of Hamzanwadi University has not can meet user satisfaction.

Prediksi Tingkat Kelulusan Mahasiswa menggunakan Algoritma Naïve Bayes, Decision Tree, ANN, KNN, dan SVM Zaenul Amri; Kusrini Kusrini; Kusnawi Kusnawi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 7 No 2 (2023): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v7i2.18620

The student graduation rate in all universities can be measured by looking at their study duration, both on time and delayed. Thus, by observing the study duration, it can affect the quality of study programs in universities. The purpose of this research is to apply and compare the Naïve Bayes, Decision Tree, Artificial Neural Network, K-Nearest Neighbor (K-NN), and Support Vector Machine (SVM) algorithms in predicting the graduation rate of students. The dataset in this research consisted of 807 student data from the Faculty of Engineering, Universitas Hamzanwadi. The data analysis technique used was descriptive statistics by applying the knowledge discovery in a database (KDD) method. The testing of the five algorithms was done by optimizing the data using the SMOTEENN technique, with a data split of 80% for training and 20% for testing, using a random state of 42. Our findings show that the Naïve Bayes algorithm had an accuracy of 92.37%, Decision Tree 91.60%, K-NN 96.95%, SVM 93.13%, and ANN 90.84%. Among the five algorithms tested, the K-NN algorithm had the highest accuracy rate of 96.95%. The predicted results tended to indicate delayed graduation influenced by GPA. Therefore, the institution needs to pay more attention to students predicted to be delayed to improve their GPA each semester, thus promoting timely graduation within the expected time frame.

PERBANDINGAN METODE OPTIMASI PENENTUAN SENTROID AWAL PADA ALGORITMA K-MEANS MENGGUNAKAN ELBOW PSO DAN SSE Muhamad Rodi; Hendrik, Hendrik; Amir Bagja; M Nurul Wathani; Zaenul Amri
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 6 No 4 (2024): EDISI 22
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v6i4.4803

The increasing volume and complexity of data present challenges in big data processing, particularly in manually identifying data patterns and relationships. In data mining, clustering methods such as the K-Means algorithm are widely used to group data based on similar characteristics. However, K-Means’ reliance on random initial centroid selection can yield suboptimal clustering results. This study aims to compare the evaluation results and iteration time of three optimization methods—Elbow, Particle Swarm Optimization (PSO), and Sum of Square Error (SSE)—on the K-Means algorithm. The dataset used is the Online Retail II dataset from the UCI Machine Learning Repository. The Davies-Bouldin Index (DBI) method is used as an evaluation tool to assess the validity of the formed clusters. Based on the analysis results, the Elbow and SSE optimization methods achieved a DBI score of 0.8500 with faster iteration times compared to PSO. Meanwhile, the PSO method provided the best DBI score of 0.7376, although it required significantly longer iteration time. The results of this study are expected to serve as a reference for selecting an appropriate optimization method for the K-Means algorithm based on time requirements and clustering evaluation outcomes.

Penerapan Temporal Convolution Network (TCN) dalam Memprediksi Harga Saham PT Bank Central Asia Tbk Wathani, M. Nurul; Amir Bagja; Muhamad Rodi; Zaenul Amri; Zulkipli
Jurnal Pendidikan, Sains, Geologi, dan Geofisika (GeoScienceEd Journal) Vol. 6 No. 1 (2025): Februari
Publisher : Mataram University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/goescienceed.v6i1.542

Studi ini bertujuan untuk meramalkan tren harga saham PT Bank Central Asia Tbk (BCA) dengan memanfaatkan algoritma Temporal Convolutional Network (TCN). TCN dipilih karena kemampuannya dalam mengenali pola temporal yang kompleks pada data deret waktu harga saham. Metode penelitian ini mencakup pengumpulan data historis harga saham BCA sebagai input untuk pelatihan dan pengujian model TCN. Pada tahap pelatihan, parameter model disesuaikan untuk meningkatkan akurasi prediksi. Evaluasi hasil dilakukan menggunakan metrik standar seperti Mean Absolute Error (MAE), Mean Square Error (MSE), dan Root Mean Square Error (RMSE), yang menunjukkan bahwa model TCN mampu memprediksi harga saham BCA dengan tingkat akurasi yang baik. Pada epoch ke-10 dan batch size 1, model mencapai nilai MAE sebesar 49, MSE sebesar 6213, dan RMSE sebesar 78. Tingkat akurasi ini memberikan wawasan yang bernilai bagi investor dan pemangku kepentingan di pasar saham. Selain itu, efektivitas model TCN dapat dianalisis lebih lanjut melalui visualisasi grafik yang membandingkan harga saham yang diprediksi dengan harga aktual, serta dengan menilai keberlanjutan dan stabilitas kinerja model dalam periode waktu tertentu. Penelitian ini berkontribusi dalam pengembangan metode prediksi harga saham dengan mengadopsi pendekatan TCN yang inovatif. Temuan ini memiliki manfaat praktis yang dapat membantu pelaku pasar dalam membuat keputusan investasi yang lebih tepat dan akurat.

Enhancing Public Sector IT Governance through COBIT 2019: A Case Study on Service Continuity and Data Management in the Central Lombok Bagja, Amir; Amri, Zaenul; Imtihan, Khairul; Rodi, Muhamad; Rusniatun, Siska Yuni
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.924

This study evaluates the IT governance maturity of the Central Lombok Civil Service Police Unit (Satpol PP) using the COBIT 2019 framework, focusing on improving service continuity and data security in a resource-constrained public sector context. The assessment, conducted across key domains such as service delivery, data security, and compliance, revealed that Satpol PP operates at Level 3 (Defined) maturity. While processes are documented and standardized, significant gaps remain in automation, proactive risk management, and real-time monitoring. These limitations hinder the organization's ability to optimize service continuity and safeguard sensitive data effectively. The study emphasizes the innovative application of COBIT 2019 in a resource-limited environment, demonstrating how the framework can be adapted to prioritize immediate needs while progressively advancing IT governance maturity. Key recommendations include automating monitoring systems, enhancing data security protocols, and implementing proactive risk management strategies. These findings contribute valuable insights into the challenges and solutions for IT governance in public institutions, providing a replicable model for similar organizations. Future research should explore the long-term impacts of these recommendations on IT governance maturity and service efficiency in other public sector contexts.

Prediksi Diabetes Menggunakan Algoritma K-Nearest (KNN) Teknik SMOTE-ENN Amri, Zaenul; Muhammad Rodi; M. Nurul Wathani; Amir Bagja; Zulkipli
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 1 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i1.27975

Nowadays, diabetes is a common disease affecting millions of people worldwide, and it is generally more prevalent among women. Recent health research has adopted various innovative and advanced technologies to diagnose individuals and predict diseases based on clinical data. One such technology is Machine Learning (ML), which enables more accurate diagnosis and prediction. The data used in this study is the Pima Indian women diabetes dataset from Kaggle and the UCI data repository. This study focuses on predicting diabetes using the KNN algorithm model by applying optimization to the dataset using the SMOTE-ENN technique to enhance prediction accuracy for Pima Indian women. The dataset was trained and tested with five different splits using Jupyter Notebook to determine the best accuracy for the KNN algorithm model. Parameters such as classification accuracy, classification error, and the ROC curve were evaluated, along with identifying the variables influencing the risk of diabetes. The results showed that applying SMOTE-ENN optimization to the research dataset significantly improved the prediction accuracy using the KNN algorithm model. With a 70% training and 30% testing data split, the model achieved a classification accuracy of 0.96, a classification error of 0.04, and an AUC of 0.95. These predictions indicated that Pima Indian women are more likely to develop diabetes due to factors such as age above 33 years, the number of pregnancies, excessive sugar consumption, blood pressure, skin thickness, insulin levels, BMI (Body Mass Index), and genetic predisposition to diabetes

SIMANKEL: A Web-based Information System for the Efficiency of Village Administration Muhammad Zamroni Uska; Arianti , Baiq Desi Dwi; Nafisah, Khufatun; Wirasasmita , Rasyid Hardi; Kholisho, Yosi Nur; Amri, Zaenul
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 1 (2025): Issues July 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i1.15055

Administrative services in Sekarteja Village are still carried out manually through physical recording, which causes data irregularity, risk of losing archives, and limited access to information for the community. This research aims to develop and evaluate the feasibility of a web-based Kelurahan Administration Management Information System (SIMANKEL) as a solution to these problems. System development uses the Research and Development (R&D) method with a waterfall process model that includes requirements analysis, design, implementation, testing, and documentation. The system was developed using PHP Native, MySQL as a database, and the user interface was designed based on UI/UX principles using Visual Studio Code. System evaluation was conducted by referring to three aspects of the ISO 9126 software quality standard, namely functionality, usability, and efficiency. The test results show that all functions run according to specifications (100%), the usability aspect obtained a feasibility score of 91% from 20 respondents (excellent category), and the efficiency aspect shows that the system load time is in the good category based on testing using GTMetrix. These findings indicate that SIMANKEL is feasible to use as an information system for administrative services in Sekarteja Village and has the potential to increase the effectiveness, efficiency, and transparency of pub services.

Penerapan Temporal Convolution Network (TCN) dalam Memprediksi Harga Saham PT Bank Central Asia Tbk Wathani, M. Nurul; Amir Bagja; Muhamad Rodi; Zaenul Amri; Zulkipli
Jurnal Pendidikan, Sains, Geologi, dan Geofisika (GeoScienceEd Journal) Vol. 6 No. 1 (2025): Februari
Publisher : Mataram University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/goescienceed.v6i1.542

Studi ini bertujuan untuk meramalkan tren harga saham PT Bank Central Asia Tbk (BCA) dengan memanfaatkan algoritma Temporal Convolutional Network (TCN). TCN dipilih karena kemampuannya dalam mengenali pola temporal yang kompleks pada data deret waktu harga saham. Metode penelitian ini mencakup pengumpulan data historis harga saham BCA sebagai input untuk pelatihan dan pengujian model TCN. Pada tahap pelatihan, parameter model disesuaikan untuk meningkatkan akurasi prediksi. Evaluasi hasil dilakukan menggunakan metrik standar seperti Mean Absolute Error (MAE), Mean Square Error (MSE), dan Root Mean Square Error (RMSE), yang menunjukkan bahwa model TCN mampu memprediksi harga saham BCA dengan tingkat akurasi yang baik. Pada epoch ke-10 dan batch size 1, model mencapai nilai MAE sebesar 49, MSE sebesar 6213, dan RMSE sebesar 78. Tingkat akurasi ini memberikan wawasan yang bernilai bagi investor dan pemangku kepentingan di pasar saham. Selain itu, efektivitas model TCN dapat dianalisis lebih lanjut melalui visualisasi grafik yang membandingkan harga saham yang diprediksi dengan harga aktual, serta dengan menilai keberlanjutan dan stabilitas kinerja model dalam periode waktu tertentu. Penelitian ini berkontribusi dalam pengembangan metode prediksi harga saham dengan mengadopsi pendekatan TCN yang inovatif. Temuan ini memiliki manfaat praktis yang dapat membantu pelaku pasar dalam membuat keputusan investasi yang lebih tepat dan akurat.

Klasifikasi Motif Batik Nusantara Menggunakan Vision Transformer (ViT) Berbasis Deep Learning Fathurrahman, Imam; Djamaluddin, Muhammad; Amri, Zaenul; Wathani, M. Nurul
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 2 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i2.31108

Batik is a cultural heritage of Indonesia that reflects local philosophies and identities through its diverse motifs. In the digital era, automatic classification of batik patterns plays a crucial role in cultural preservation, education, and commercialization. This study aims to develop a batik motif classification system using Vision Transformer (ViT), a deep learning architecture based on self-attention capable of capturing global spatial relationships in images. The dataset comprises 800 images spanning 20 batik motif classes from various regions, divided into training and testing subsets. The ViT model was fine-tuned using pretrained weights from ImageNet-21k, with standard preprocessing and data augmentation applied to the training set. Model performance was evaluated using accuracy, precision, recall, F1-score, confusion matrix, and prediction visualization. Results indicate that ViT achieved an overall accuracy of 96%, with most classes recording F1-scores above 0.90. Evaluation on unseen batik images demonstrated excellent generalization capability, achieving 99.94% confidence in prediction. These findings suggest that ViT is an effective and efficient architecture for batik motif classification and offers valuable contributions to cultural preservation through artificial intelligence.

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