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All Journal Techno.Com: Jurnal Teknologi Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Indonesian Journal on Software Engineering (IJSE) Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Jurnal Teknik Komputer AMIK BSI JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURIKOM (Jurnal Riset Komputer) Jurnal Riset Informatika Systemic: Information System and Informatics Journal TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer Indonesian Journal of Business Intelligence (IJUBI) Jurnal Infortech Jurnal Scientia Akrab Juara : Jurnal Ilmu-ilmu Sosial Jurnal Sistem Informasi RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Bulletin of Computer Science Research Computer Science (CO-SCIENCE) INSANtek – Jurnal Inovasi dan Sains Teknik Elektro JURNAL ELEKTRO DAN INFORMATIKA SWADHARMA (JEIS) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Hexagon Jurnal Ticom: Technology of Information and Communication
Omar Pahlevi
Universitas Bina Sarana Informatika
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences

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Optimasi Algoritma Random Forest Dengan Fitur Seleksi Backward Elimination Untuk Penilaian Kelayakan Kredit Amrin Amrin; Omar Pahlevi; Sismadi Sismadi
Jurnal Ticom: Technology of Information and Communication Vol 13 No 3 (2025): Jurnal Ticom-Mei 2025
Publisher : Asosiasi Pendidikan Tinggi Informatika dan Komputer Provinsi DKI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70309/ticom.v13i3.151

Abstract

Kredit sekarang menjadi tren di masyarakat. Problem kredit adalah sejarah penggunaan kartu kredit yang salah. Dampak yang ditimbulkan dapat menyebabkan kredit macet. Jika pelanggan tidak membayar utang yang telah disepakati dengan bank, mereka dapat meningkatkan risiko kredit mereka. Dalam penelitian ini, peneliti menerapkan algoritma Random Forest tanpa optimasi dan Algorima Random Forest dengan Optimasi Fitur Seleksi Backward Elimination untuk mengklasifikasikan status kelayakan kredit. Peneliti menggunakan 481 catatan kredit kendaraan dengan ulasan ”bad” dan ”good”. Variabel independen digunakan dalam penelitinan adalah status tanggungan, usia, pendidikan terkahir, status pernikahan, pekerjaan, status perusahaan, pendapatan, status pekerjaan, kondisi rumah, lama tinggal dan uang muka. Dari hasil penelitian dan pengujian, performa model random forest tanpa backward elimination untuk penilaian kelayakan kredit memberikan tingkat akurasi kebenaran sebesar 78,60% dengan nilai area under the curva (AUC) sebesar 0,907. Sedangkan Performa model  random forest dengan backward elimination memberikan tingkat akurasi kebenaran sebesar 89,81% dengan nilai area under the curve (AUC) sebesar 0,922. Hal ini membuktikan bahwa optimasi dengan backward elimination dapat meningkatkan kinerja metode klasifikasi yang digunakan.
Optimasi Algoritma Naïve Bayes Berbasis Particle Swarm Optimization Untuk Klasifikasi Status Stunting Pahlevi, Omar; Amrin, Amrin; Handrianto, Yopi
Computer Science (CO-SCIENCE) Vol. 4 No. 1 (2024): Januari 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i1.2963

Abstract

Every parent wants their children to grow up healthy. Eating a healthy diet can minimize stunting. Long-term nutritional deficiencies can lead to stunting, a chronic nutritional problem that impairs physical growth and development, including low body weight and height. Preventive action against stunting is a fundamental activity that must be done immediately in the form of counseling and taking further medical action.  In data mining there are several methods for extracting information including classification. There are various methods for extracting information using data mining, such as classification. In this research, researchers will apply Naïve Bayes with Particle Swarm Optimization (PSO) for the classification of stunting status in order to determine whether a child has a case of stunting or not based on gender, age, birth weight, body weight, body length, and breastfeeding. In the final results of the research, it is known that the accuracy of the truth obtained through the performance of the Naïve Bayes algorithm model is 80.69% and a score of 0.801 resulting from Area Under the Curva (AUC). Then based on the calculation results with the Naïve Bayes algorithm model with Particle Swarm Optimization, it can be obtained a truth accuracy rate of 83.06% with an Area Under the Curve (AUC) value of 0.801. Based on the final value obtained, the pattern of applying Particle Swarm Optimization to the Naïve Bayes algorithm can improve the performance of the classification method used in this research activity.
Analisa Komparasi Model Data Mining Algoritma C4.5, CHAID, dan Random Forest Untuk Penilaian Kelayakan Kredit Amrin, Amrin; Pahlevi, Omar; Rianto, Harsih
Computer Science (CO-SCIENCE) Vol. 5 No. 1 (2025): Januari 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i1.6208

Abstract

Credit has now become a trend in society. The problem with credit is the improper history of credit card usage. The resulting impact can lead to bad credit. If customers fail to pay off debts that have been agreed upon with the bank, they can increase their credit risk. This study aims to conduct a comparative analysis of three data mining classification methods: the C4.5 algorithm, Chi-Squared Automatic Interaction Detection (CHAID), and Random Forest. The goal is to classify creditworthiness status. The researcher used 481 vehicle credit records with "bad" and "good" reviews. In this study, the independent variables used are dependent status, age, marital status, occupation, income, employment status, company status, last education, length of stay, house condition, and down payment. For creditworthiness assessment, the C4.5 model shows a truth accuracy rate of 91.90% with an area under the curve (AUC) value of 0.915. The CHAID model shows a truth accuracy rate of 63.83% with an AUC value of 0.661, and the Random Forest model shows a truth accuracy rate of 78.60% with an AUC value of 0.907. The evaluation results show that both the Random Forest and C4.5 algorithms have high accuracy rates and AUC values.
Comparative Analysis of Machine Learning Model Performance for Classification of Edible or Non-edible Mushrooms Pahlevi, Omar; Sriyadi, Sriyadi
JURNAL TEKNIK KOMPUTER AMIK BSI Vol 11, No 2 (2025): Periode Juli 2025
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jtk.v11i2.26007

Abstract

Mushrooms provide significant nutritional benefits and play a crucial role in the global food industry. However, not all mushroom species are safe for consumption, as some contain toxic compounds that can cause severe poisoning and even death. Accurate identification is essential to differentiate between edible and poisonous mushrooms. Traditional classification methods relying on manual morphological identification are often inaccurate, especially when toxic and edible mushrooms have similar physical characteristics. Machine Learning (ML) technology offers an innovative solution to enhance classification accuracy and improve safety in mushroom consumption. This study compares the performance of three major classification algorithms—Random Forest, Logistic Regression, and Naïve Bayes—using an open dataset from Kaggle. The analysis was conducted using the KNIME platform, evaluating the algorithms based on accuracy, sensitivity, and computational efficiency. The results indicate that Random Forest achieved the highest accuracy at 98.90%, followed by Logistic Regression at 69.67% and Naïve Bayes at 55.46%. These findings highlight the superiority of ensemble methods in classification tasks. This research contributes to the development of a reliable ML-based mushroom classification system. However, limitations remain, such as the exclusion of other high-performance algorithms like Support Vector Machine and Artificial Neural Networks. Future studies may incorporate optimization techniques to improve model performance. Additionally, implementing this classification system into mobile or web-based applications could provide broader benefits by enabling quick identification of mushrooms, minimizing health risks, and improving consumer confidence in mushroom safety.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN VENDOR IT MAINTENANCE MENGGUNAKAN PENDEKATAN WASPAS Fatmayati, Fryda; Riyanto, Umbar; Rahmadian, Jefri; Pahlevi, Omar
TEKNOKOM Vol. 7 No. 1 (2024): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/teknokom.v7i1.199

Abstract

IT maintenance is important for a company because these activities are carried out to maintain and support the optimal performance of a company's IT systems and infrastructure. Generally, selecting an IT maintenance vendor is done by collecting vendor data and then evaluating it based on the desired criteria. This, of course, results in the time it takes to make a choice and makes it difficult to determine the best option. If you choose the wrong IT maintenance vendor, it will result in disrupting the company's stability. This research was conducted with the aim of developing a decision support system that can be used to determine the IT maintenance vendor that best suits their needs and preferences using the WASPAS (Weighted Aggregated Sum Product Assessment) approach. This method is used to determine the best option through weighted addition and multiplication, producing a final value that reflects the extent to which each option can meet the specified criteria. From the existing case studies, the best alternative was obtained, namely: Microsis (A4) got a preference value of 0.8769, followed by ICT Canopy (A1) with a preference value of 0.8613, Pillar IT (A2) with a preference value of 0.8408, Indocom Niaga (A5) with a preference value of 0.8180, and Sasana Digital (A3) with a preference value of 0.7389. The usability test carried out received a score of 88%, which indicates that the system is suitable for use.
Optimasi Algoritma C4.5 dan Naïve Bayes Berbasis Particle Swarm Optimization Untuk Diagnosa Penyakit Peradangan Hati Amrin, Amrin; Pahlevi, Omar; Satriadi, Irawan
INSANtek Vol. 2 No. 1 (2021): Mei 2021
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/instk.v2i1.399

Abstract

Peradangan hati merupakan salah satu penyakit menular yang menjadi masalah kesehatan masyarakat yang berpengaruh terhadap angka kesakitan, angka kematian, status kesehatan masyarakat, angka harapan hidup, dan dampak sosial ekonomi lainnya. Melakukan diagnosa dini pada penyakit ini adalah sesuatu yang sangat penting agar dapat secara cepat ditangani dan diobati. Pada penelitian ini penulis akan mengaplikasikan dan membandingkan beberapa metode klasifikasi data mining dan optimasi dengan particle swarm optimization (pso), diantaranya Algoritma C4.5, Naïve Bayes, C4.5 dengan pso, dan Naïve Bayes dengan pso untuk mendiagnosis penyakit peradangan hati, kemudian membandingkan mana dari beberapa metode tersebut yang paling akurat. Berdasarkan hasil penelitian, diketahui bahwa metode C4.5 dengan pso merupakan metode terbaik dengan akurasi 79,51% dan nilai under the curva (AUC) 0,950, kemudian metode Naive Bayes dengan pso memiliki akurasi 79,28% dan nilai AUC sebesar 0,739, kemudian metode C4.5 dengan tingkat akurasi sebesar 70,99% dan nilai AUC sebesar 0,950, selanjutnya metode Naive Bayes dengan tingkat akurasi sebesar 66,14%, dan nilai AUC sebesar 0,742. Hal ini membuktikan bahwa optimasi particle swarm optimization dapat meningkatkan kinerja metode klasifikasi yang digunakan
Penggunaan Algoritma K-Means dalam Mengelompokkan Jumlah Usaha dan Hasil Pendapatan UMKM DKI Jakarta Sunarti, Sunarti; Alawiah, Enok Tuti; Pahlevi, Omar
Techno.Com Vol. 24 No. 4 (2025): November 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i4.14925

Abstract

UMKM Provinsi DKI Jakarta berkontribusi signifikan terhadap perekonomian daerah. Namun, ada permasalahannya pemetaan data lemah, segmentasi usaha kurang, belum adanya pemetaan yang jelas mengenai jumlah usaha dan pendapatan, keterbatasan akses data dan validitas informasi, serta belum adanya diferensiasi program pembangunan berdasarkan karakteristik usaha. Tujuan penelitian adalah menganalisis data jumlah usaha dan hasil pendapatan UMKM menggunakan metode K-means. Metode ini digunakan mengelompokkan jumlah usaha dan hasil pendapatan. Hasil klasterisasi menghasilkan tiga klaster wilayah: Klaster (1) yaitu kota administratif Jakarta Pusat, Jakarta Selatan, dan Jakarta Timur, memiliki jumlah usaha dan pendapatan tertinggi. Klaster(2) yaitu kota administratif Jakarta Barat dan Jakarta Utara, memiliki jumlah usaha dan pendapatan sedang. Klaster(3) yaitu kota administratif Kepulauan Seribu, memiliki jumlah usaha dan pendapatan yang rendah. Hasil evaluasi proses klasterisasi mempergunakan Davies Bouldin Index (DBI) bernilai -0.390. Hasil ini menunjukkan K-Means dapat memetakan jumlah usaha dan hasil pendapatan, sehingga memudahkan dalam menetapkan strategis kebijakan, mengembangkan usaha, dan menyusun strategi pemasaran berdasarkan karakteristik ekonomi setiap daerah.   Kata Kunci – UMKM, Provinsi DKI Jakarta, Klasterisasi, Metode K-Means
Co-Authors Agni Isador Harsapranata Amrin Amrin Amrin Amrin Astriana Mulyani Atmojo, Muhamad Kusumo Bachtiar, Dimas Agung Cucu Ika Agustyaningrum Danny Ong Dewi Ayu Nur Wulandari Dony Ardiyansah Enok Tuti Alawiah Fahrizal Fahrizal Faiz Octa Reynaldi Fryda Fatmayati Harmen Harmen Harriansyah Harriansyah Harsih Rianto Henny Leidiyana Himawan Wijaya Imelda Sari Indah Suryani Ita Dewi Sintawati Ita Dewi Sintawati Luci Kanti Rahayu Miftahul Khoir Muhammad Syafrizal Rahmadian, Jefri Renal Verdiyan Erlangga Reni Widyastuti Riyanto, Umbar Rizka Dahlia Rony, Zahara Tussoleha Satriadi, Irawan Sismadi Sismadi Sriyadi Sriyadi, Sriyadi Sugandi, Anton Sugiharja, Danur Sunarti Syamsiar, Syamsiar Tri Santoso Tri Santoso Yopi Handrianto, Yopi