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All Journal Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Jurnal Mitra Manajemen Jurnal Sains dan Teknologi: Jurnal Keilmuan dan Aplikasi Teknologi Industri Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Jurnal Mantik JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurnal Informatika dan Rekayasa Elektronik JPKMI (Jurnal Pengabdian Kepada Masyarakat Indonesia) Computer Science (CO-SCIENCE) Jurnal Pengabdian Masyarakat Formosa (JPMF) Paradigma International Journal of Emerging Issues in Islamic Studies semanTIK
Abdul Hamid
Universitas Bina Sarana Informatika
Religion Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Public Health Social Sciences

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REVOLUSI DIAGNOSIS: OPTIMASI RANDOM TREE-PSO UNTUK PENYAKIT GINJAL KRONIS Sartini; Sumarna; Abdul Hamid; Ahmad Hafidzul Kahfi; Nicodias Palasara
Jurnal Informatika dan Rekayasa Elektronik Vol. 8 No. 1 (2025): JIRE APRIL 2025
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/jire.v8i1.1542

Abstract

Penyakit ginjal kronis (PGK) merupakan salah satu masalah kesehatan serius yang memerlukan deteksi dini untuk mencegah komplikasi lebih lanjut dan meningkatkan kualitas hidup pasien. Penelitian ini bertujuan mengembangkan model prediksi PGK dalam upaya meningkatkan akurasi diagnosis dini PGK dengan dataset yang digunakan diperoleh dari UCI Repository. Metode yang dipakai berbasis algoritma Random Tree yang dioptimasi menggunakan Particle Swarm Optimization (PSO) yang berfungsi sebagai metode optimasi untuk meningkatkan kinerja model dengan kedalaman pohon dan jumlah atribut yang dipertimbangkan pada setiap pemisahan, untuk menemukan konfigurasi yang menghasilkan akurasi tertinggi. Proses pengembangan model mencakup tahap seleksi fitur, pelatihan model, dan evaluasi performa menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa algoritma Random Tree yang dioptimasi PSO secara signifikan meningkatkan performa prediksi dibandingkan model baseline, dengan akurasi mencapai 94,25%. Optimalisasi ini juga mengurangi kompleksitas model tanpa mengorbankan akurasi. Temuan ini menunjukkan potensi penerapan model yang diusulkan untuk mendukung sistem pengambilan keputusan medis secara lebih efisien, terutama dalam deteksi dini PGK. Rekomendasi lebih lanjut mencakup integrasi model ini pada sistem berbasis teknologi di lingkungan klinis untuk mengurangi beban kerja tenaga medis.
Pengoptimalan Seleksi Fitur Berbasis Particle Swarm Optimization pada Prediksi Gagal Jantung dengan Random Tree Ridwansyah Ridwansyah; Sri Rahayu; Jajang Jaya Purnama; Verry Riyanto; Abdul Hamid
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 8 No. 1 (2026): Juni 2026
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v8i1.16595

Abstract

Heart failure is one of the leading causes of hospitalization and mortality, particularly among older adults. Early detection is essential to support effective clinical decision-making. This study aims to develop a heart failure prediction model using the Random Tree classification algorithm optimized with Particle Swarm Optimization (PSO) for feature selection. Random Tree was chosen for its simplicity and interpretability, while PSO was employed to identify the most relevant features and remove less important ones. The dataset was obtained from the UCI Machine Learning Repository and consists of 299 patient records with 12 clinical attributes. Model performance was evaluated using accuracy, precision, recall, and Area Under the Curve (AUC). The baseline Random Tree model achieved an accuracy of 75.58% and an AUC of 0.632. After applying PSO-based feature selection, the optimized model achieved an accuracy of 82.27% and an AUC of 0.740. These findings indicate that integrating PSO with Random Tree effectively improves heart failure prediction performance and has potential as a clinical decision-support tool
The First Android Based Sharia Fintech Innovation in Indonesia to Increase Inclusive and Literate on Society’s Finance Suhartono Suhartono; Juniato Sidauruk; Octa Pratama Putra; Syamsul Bahri; Martias Martias; Aan `Rahman; Abdul Hamid; Lukman Hakim; Indria Widyastuti; Badurrachman Abdurrachman; Ninuk Riesmiyantiningtias; Rizky Amalia; Indra Chaidir
International Journal of Emerging Issues in Islamic Studies Vol. 1 No. 2 (2021): December 2021
Publisher : Research Synergy Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31098/ijeiis.v1i2.703

Abstract

Technology has become the part of today’s people life. Then, it is actually close to the application of it. Absolutely, it has example; such as the electricity for having more sophisticated in financial technology (Fin-Tech). The simplicity and speed of this technology have led people to adopt it in everyday’s life. One of the innovations in developing business and the economy, especially in the banking sector, is currently to develop Fintech (Financial Technology) which is able to facilitate all types of buying and selling transactions, investments and fundraising. Next, the purpose of this study is to explain and provide an understanding of the technical, procedures and benefits of the application, it is called Sharia FinTech. Then, it is also to contribute to the literature on the capacity of the latest technological and non-technological innovations. The research method used is descriptive research method with a qualitative approach. It is to describe and explore the phenomena in the form of engineering human innovation in the financial technology industry. It is done by taking into account the characteristics, quality, and interrelationships between activities It has several aspects; they are: conducting the observation, having an interview session, creating the documentation, and the last one is doing the Literature review. The result of this study is to increase the knowledge, skills and confidence of the community in managing personal finances to be better and to provide access to be having convenient and accountable financial services. Afterwards, this study linits on explaining and providing an understanding of the technical, procedure and benefits of Sharia Fintech for all people in need. Thence, the limitation of the research only discusses the role of Islamic Fintech in increasing the public financial inclusion and literacy. As for the the next researchers, they can be even wider by adding the collaboration of fintech and the banking world. The novelty of this research is the use of the android application as a digital platform in financial inclusion and literacy.
Grouping Data in Predicting Infant Mortality Using K-Means and Decision Tree Ridwansyah Ridwansyah; Verry Riyanto; Abdul Hamid; Sri Rahayu; Jajang Jaya Purnama
Paradigma - Jurnal Komputer dan Informatika Vol. 24 No. 2 (2022): September 2022 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/paradigma.v24i2.1399

Abstract

Death is something that we cannot avoid where, when and how death comes. The high infant mortality rate is the main thing and the Indonesian government must prioritize, one of the government's efforts to reduce infant mortality is by conducting a surveillance program, namely PWS KIA where the program is uniting the health of mothers and babies in the local area, basically there are several infant deaths that have causes from the time of pregnancy, accidents, disasters, diseases or because it is destiny from God, for that research is carried out in classifying infant mortality data. For grouping infant mortality data, a K-Means method is needed to analyze data by carrying out a data modeling process without supervision or also known as unsupervised learning. In showing the centroid in the early stages of the k-means algorithm, it is very influential on the results of the cluster carried out on the infant mortality dataset. taken from data.go.id with different centroid results. The results of the clustering model pattern that can be trusted by the government or the Health department to prevent infant mortality. From the clustering results, four labels are tested again using the decision tree algorithm.
Optimizing Heart Failure Detection: A Comparison between Naive Bayes and Particle Swarm Optimization Abdul Hamid; Ridwansyah Ridwansyah
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 1 (2024): March 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i1.3284

Abstract

This research focuses on the importance of early detection of heart failure which is a serious global health problem. Given the variety of symptoms of heart failure, accurate early detection methods are needed with the aim of reducing the impact of this disease. This study uses the Naïve Bayes (NB) method which has been proven effective in classifying heart failure with significant variations in accuracy by integrating Particle Swarm Optimization (PSO) to improve the model. The evaluation model involves a confusion matrix including accuracy, precision, recall, and Area Under the Curve. The research results show that the integration of PSO in NB results in an increase in accuracy of 7.73%, an increase in precision of 6.42%, and an increase in recall of 1.93%. Although there was a small decrease in AUC. This research shows that the success of NB with PSO can help improve the performance of early detection of heart failure. This indicates the importance of this research in developing more accurate and effective detection methods for critical health conditions such as heart failure.
Co-Authors Aan `Rahman Ahmad Hafidzul Kahfi Ahmad Hafidzul Kahfi Ali Martondi Badurrachman Abdurrachman Ganda Wijaya Ginabila Hendri Mahmud Nawawi Henny Destiana Hilda Rachmi Imam Nawawi Indra Chaidir, Indra Indria Widyastuti Jajang Jaya Purnama Lukman Hakim Lukman Hakim Martias Martias Mochammad Abdul Azis Muhammad Iqbal Muhammad Rezki Nicodias Palasara Ninuk Riesmiyantiningtias Numan Musyaffa Octa Pratama Putra Putie Maharani Putie Maharani Basa Rachmat Suryadithia Ricki Sastra Ridwansyah Ridwansyah Ridwansyah Ridwansyah Ridwansyah Ridwansyah Rizky Amalia Sartini Sidauruk, Juniato Sri Rahayu Sri Rahayu Sugiono Suhardjono S Suhartono Suhartono Sumarna Syamsul Bahri Syamsul Bahri Verry Riyanto