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All Journal Journal of Information Systems and Informatics Infotek : Jurnal Informatika dan Teknologi Jurnal Pendidikan, Sains, Geologi, dan Geofisika (GeoScienceEd Journal) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Teknologi Informasi untuk Masyarakat (Jurnal Teknokrat) Pekayunan
Amir Bagja
Universitas Hamzanwadi
Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Control & Systems Engineering Education Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Physics

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Komparasi Algoritma Naïve Bayes Dan Support Vector Machine (SVM) Untuk Klasifikasi Kelayakan Pemberian Pinjaman Amir Bagja; Kusrini Kusrini; Muhammad Rudyanto Arief
Infotek: Jurnal Informatika dan Teknologi Vol. 6 No. 2 (2023): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v6i2.20059

Abstract

Cooperatives are social organizations or economic bodies that have a very important role in the growth, development of economic potential and community success. One of the cooperative activities is the provision of credit or loans to community members. Cooperative credit is one of the most important banking activities and serves to provide credit to the community. In practice, errors often arise due to inaccurate credit analysis, or the behavior of the customers themselves. The purpose of this research is to compare the accuracy results between the Naive Bayes algorithm and Support Vector Machine (SVM), where the best accuracy results can later be used as a reference to determine the profitability of lending. The attributes used in this study consist of 11 attributes, namely: Gender, marital status, occupation, relatives, nominal income, income criteria, loan amount, loan term, interest rate, installments and class as income characteristics. The dataset used in this study includes 166 members of the Daru Nahdla Capita Shari'ah cooperative. The results of testing the naive bayes algorithm after dividing the data five times, dividing the data set 70% as test data and 30% as training data, obtained a precision value of 97.00%, recall 100.00%, F1 score 99.00%. and accuracy 98.00%. Thus, the Naive Bayesian algorithm is an algorithm that shows accurate classification and prediction
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

Abstract

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

Abstract

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

Abstract

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

Abstract

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
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

Abstract

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.
Pengembangan Model AI Menggunakan Algoritma Intensity Of Character (IoC) dan Reduced Support Vector Machine (RSVM) untuk Transliterasi Citra Aksara Sasak Samsu, Lalu Muhammad; Hidayat, Muh.Adrian Juniarta; Bagja, Amir; Saiful, Muhammad
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.v8i2.31124

Abstract

The limited human resources who are able to read Sasak script palm leaves are the main motivation for developing a transliteration tool for Sasak script images on palm leaves. By using the Reduced Support Vector Machine or RSVM algorithm as one of the classification methods, transliteration efforts can be facilitated with maximum results. The principle of the RSVM method in classifying objects by separating two different classes using a hyperplane has been proven to be able to produce maximum accuracy performance in this study. The research data in the form of Sasak script images resulting from the palm leaf image segmentation process that has been divided into 18 classes. The feature extraction algorithm used is Intensity of Character (IoC) with window sizes of 3x3, 4x4, and 5x5 and 3-Fold, 5-Fold, 7-Fold data Imbalance. The test results at the RSVM classification stage using the Linear Kernel, Polynomial Kernel,  Radial Basic Function (RBF) and One against One modeling on the 18 classes tested, where each class contains 20 handwritten Jejawen Sasak script image data on palm leaves, were recorded to produce the highest accuracy, which was 93.6%.
Pelatihan Jaringan Berbasis Mikrotik Untuk Peningkatan Kompetensi Siswa kelas XI di SMKN 1 Pringgasela Amri Muliawan Nur; Hariman Bahtiar; Yahya; Nurhidayati; Almi Yulistia Alwanda; Amir Bagja
Jurnal Teknologi Informasi untuk Masyarakat Vol. 2 No. 2 (2024): Jurnal Teknologi Informasi untuk Masyarakat (Teknokrat)
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jt.v2i2.28380

Abstract

Computer networks are one of the important subjects in the field of information technology. Various types of computer network devices continue to innovate to adapt to technological developments. Mikrotik is one of the router products that is widely used to implement a network system. As an educator, in this case a lecturer is required to implement the Tri Darma every year, one of which is PKM, namely Community Service, which this year is focused on the Computer Department at SMKN 1 Peringgasela. The aim of this training is to increase understanding and competence in the field of computer networks using Mikrotik, and to prepare students to face the Skills Competency Examination (UKK) as well as providing preparation for the implementation of the PSG and when they graduate. The training methods used are lectures and practice (Demostration). The results of this training activity in general, apart from the enthusiasm and enthusiasm of the students, include several components, namely: 1) Success in training, 2) Achievement of planned research objectives, 3) Achievement of planned material targets
MENINGKATKAN LITERASI DIGITAL MAHASISWA MELALUI PELATIHAN TEKNOLOGI INFORMASI Amir Bagja; Zaenul Amri; M Nurul Wathani; Muahamd Rodi
Jurnal Pekayunan Vol. 1 No. 3 (2025): PEKAYUNAN Maret 2025
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/c9ejb090

Abstract

Perkembangan teknologi informasi telah memberikan dampak yang signifikan dalam berbagai aspek kehidupan, termasuk pendidikan dan organisasi mahasiswa. Kegiatan ini bertujuan untuk meningkatkan pemahaman mahasiswa terkait konsep dasar teknologi informasi serta penerapannya dalam lingkungan akademik dan sosial. Metode yang digunakan dalam kegiatan ini adalah penyampaian materi secara interaktif serta diskusi untuk menggali lebih dalam pemahaman peserta. Hasil kegiatan menunjukkan bahwa pemahaman peserta terhadap teknologi informasi meningkat secara signifikan, serta mereka mampu mengidentifikasi tantangan dan solusi dalam pemanfaatan teknologi tersebut. Kegiatan ini juga mendapatkan respon positif dari peserta, yang menunjukkan bahwa metode yang digunakan efektif dalam meningkatkan literasi digital. Kesimpulannya, kegiatan ini memberikan wawasan yang luas mengenai teknologi informasi dan dapat menjadi model bagi kegiatan serupa di masa mendatang.
PELATIHAN FITUR OTOMATISASI DOKUMEN MICROSOFT WORD UNTUK MENINGKATKAN KETERAMPILAN PENYUSUNAN DOKUMEN AKADEMIK DI STMIK LOMBOK Zulkipli; Amir Bagja; Zaenul Amri; M Nurul Wathani; Muhamad Rodi
Jurnal Pekayunan Vol. 1 No. 4 (2025): PEKAYUNAN April 2025
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/k6qt8537

Abstract

This training activity aims to enhance participants' skills in utilizing Microsoft Word’s document automation features, including automatic table of contents creation, figure listing, page numbering, and cross-referencing. The training was conducted in the REC Room of STMIK Lombok on Wednesday, May 28, 2025, involving 40 participants. The method used was a hands-on approach (learning by doing), and participants were evaluated through pre-test and post-test assessments. The pre-test results showed that 17 participants were in the "not capable" category, and none were categorized as "highly capable". After the training, the post-test revealed significant improvement, with 33 participants achieving the "highly capable" category. This activity has proven effective in equipping participants with essential document processing skills needed in academic settings. It is recommended that similar training be held regularly within campus environments or other institutions aiming to strengthen digital academic literacy.
Co-Authors Almi Yulistia Alwanda Bahtiar, Hariman Hendrik Hendrik Hidayat, Muh.Adrian Juniarta Khairul Imtihan Kusrini Kusrini M. Nurul Wathani Muahamd Rodi Muhammad Rodi Muhammad Rudyanto Arief Muhammad, Saiful Nur, Amri Muliawan Nurhidayati Rusniatun, Siska Yuni Samsu, Lalu Muhammad Yahya Zaenul Amri Zulkipli