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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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Comparison of Conventional Machine Learning and Deep Neural Network Algorithms in the Prediction of Monkey-Pox Cucu Ika Agustyaningrum; Rizka Dahlia; Omar Pahlevi
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.522

Abstract

Smallpox syndrome, also known as monkeypox, is an uncommon zoonotic viral infection brought on by the monkeypox virus, which belongs to the genus orthopoxvirus and family Poxviridae. Injury-related mortality in primates ranges from 1 to 10%. Data mining is a method for analyzing data. Deep neural networks and traditional machine learning methods are both used in the data analysis process. The Python programming language is used during the comparison procedure of this research algorithm to generate values for accuracy, f1 score, precision, recall, ROC, and AUC. The test results demonstrate that using sigmoid activation function parameters, the deep neural network algorithm's accuracy is 70.08%, F1 score is 79.18%, precision is 68.59%, recall is 62.65%, and AUC is 62.65%. In comparison to using conventional machine learning algorithms, the adagrad optimizer with learning rate 0.01 and 0.2 dropout has a higher value. The conventional machine learning model algorithm has the best xgboost, F1 score, precision, recall, and AUC scores when compared to other approaches: 64.40%, 64.45%, and 78.14%. According to these numbers, the average fairness disparity between deep neural network algorithms and traditional machine learning is 5.68%, F1 score is 13.79%, precision is 4.14%, recall is 1.75%, and AUC is 1.75%.
Implementasi Algoritma Klasifikasi Random Forest Untuk Penilaian Kelayakan Kredit Omar Pahlevi; Amrin Amrin; Yopi Handrianto
Jurnal Infortech Vol 5, No 1 (2023): JUNI 2023
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/infortech.v5i1.15829

Abstract

Kredit merupakan penyediaan uang  atau  tagihan  dengan  adanya  suatu  persetujuan  atau  kesepakatan  antara  pihak  penyedia  kredit dengan   pihak   peminjam   untuk   melunasi   utangnya   berdasarkan   jangka   waktu   tertentu   dengan pemberian  bunga. Melakukan klasifikasi kelayakan kredit sesuatu yang sangat penting agar dapat mengetahui data kredit  kendaraan bermotor  baik yang bermasalah maupun yang tidak  bermasalah. Dataset yang digunakan sebanyak 481 record data kredit  kendaraan bermotor  baik yang bermasalah maupun yang tidak  bermasalah. Variabel input pada penelitian ini terdiri dari tiga belas variabel, diantaranya status perkawinan, jumlah tanggungan, umur, status tempat tinggal, kepemilikan rumah, pekerjaan, status pekerjaan, status perusahaan, penghasilan, uang muka, pendidikan, lama tinggal, dan kondisi rumah.  Pada penelitian ini, peneliti akan mengimplementasikan metode klasifikasi data mining yaitu random forest untuk klasifikasi kelayakan kredit. Berdasarkan hasil pengukuran kinerja performa model algoritma Random Forest untuk klasifikasi kelayakan kredit memberikan tingkat akurasi kebenaran sebesar 78,60% dengan nilai Area Under the Curve (AUC) sebesar 0,907. Berdasarkan tingkat akurasi dan nilai Area Under the Curve (AUC), maka model algoritma Random Forest termasuk kategori klasifikasi sangat baik.
Penerapan Manajemen Pendidikan Berbasis Kurikukum 2013 di SMK Tanjung Priok 1 Jakarta Pahlevi, Omar; Widyastuti, Reni
Perspektif : Jurnal Ekonomi dan Manajemen Akademi Bina Sarana Informatika Vol 16, No 2 (2018): September 2018
Publisher : www.bsi.ac.id

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (621.334 KB) | DOI: 10.31294/jp.v16i2.3948

Abstract

 Organisasi sekolah membutuhkan manajemen yang baik untuk kelangsungan operasionalnya. SMK Tanjung Priok 1 Jakarta merupakan sekolah yang berdiri dari tahun 1973. Penelitian ini bertujuan untuk mendeskripsikan dan menganalisis: (1) manajemen kurikulum dan pembelajaran; (2) Manajemen siswa; (3) Manajemen pendidik dan tenaga kependidikan; (4) Pengelolaan sarana dan prasarana; (5) Pengelolaan pembiayaan di SMK Tanjung Priok 1 Jakarta. Metode penelitian menggunakan pendekatan deskriptif kualitatif. Teknik pengumpulan data dilakukan dengan wawancara, dokumentasi dan observasi. Hasil dari penelitian ini adalah: 1. Manajemen kurikulum 2013 dan pembelajaran di SMK Tanjung Priok 1 Jakarta dilaksanakan dengan menggunakan fungsi manajemen. 2. Manajemen siswa berjalan dengan baik. 3. Manajemen pendidik dan tenaga kependidikan telah dilaksanakan baik berdasarkan fungsi manajemen. 4. Fasilitas dan manajemen infrastruktur sesuai dengan standar manajemen. 5. Manajemen Pembiayaan dikelola dalam akuntabilitas.
Comparison of Conventional Machine Learning and Deep Neural Network Algorithms in the Prediction of Monkey-Pox Cucu Ika Agustyaningrum; Rizka Dahlia; Omar Pahlevi
Jurnal Riset Informatika Vol. 5 No. 3 (2023): June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.217

Abstract

Smallpox syndrome, or monkeypox, is an uncommon zoonotic viral infection brought on by the monkeypox virus, which belongs to the genus Orthopoxvirus and family Poxviridae. Injury-related mortality in primates ranges from 1 to 10%. Data mining is a method for analyzing data. Deep neural networks and traditional machine learning methods are used in data analysis. The Python programming language is used during the comparison procedure of this research algorithm to generate values for accuracy, f1 score, precision, recall, ROC, and AUC. The test results demonstrate that using sigmoid activation function parameters, the deep neural network algorithm's accuracy is 70.08%, F1 score is 79.18%, precision is 68.59%, recall is 62.65%, and AUC is 62.65%. Compared to conventional machine learning algorithms, the adagrad optimizer has a higher value with a learning rate of 0.01 and 0.2 dropouts. The conventional machine learning model algorithm has the best xgboost, F1 score, precision, recall, and AUC scores compared to other approaches: 64.40%, 64.45%, and 78.14%. According to these numbers, the average fairness disparity between deep neural network algorithms and traditional machine learning is 5.68%, F1 score is 13.79%, precision is 4.14%, recall is 1.75%, and AUC is 1.75%.
BLOCKCHAIN IN THE INTERNET OF THINGS : BIBLIOMETRIC ANALYSIS Agni Isador Harsapranata; Omar Pahlevi
Jurnal Scientia Vol. 12 No. 04 (2023): Education, Sosial science and Planning technique, 2023, Edition September-Nov
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/scientia.v12i04.2087

Abstract

The development of the Internet of Things is so rapid that it can be seen by using it to help human life. It can be seen in various health, education, plantations, agriculture, fisheries and other fields. With the Internet of Things in most aspects of human life, reliable data integrity is required, thus encouraging technological verification to ensure data integrity. In this research, the author uses bibliometric analysis using Vosviewer and Biblioshiny to determine the picture of blockchain and Internet of Things research. The author uses 11,302 journal data from 2016 to 2023; it can be seen that the highest H index ranking is given by the IEEE Internet of Things Journal", with a value of 65. This analysis shows that research is developing well but is still quite uniquely carried out in the "Home Environments Fabric" field. In contrast, the basis research is "Blockchain Internet IoT".
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 | Full PDF (432.442 KB) | 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
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.
Pengembangan Sistem Informasi Personal Finance Management Menggunakan Pendekatan Rapid Application Development Pahlevi, Omar; Amrin; Handrianto, Yopi
Resolusi : Rekayasa Teknik Informatika dan Informasi Vol. 4 No. 5 (2024): RESOLUSI May 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/resolusi.v4i5.1883

Abstract

In the increasingly advanced digital era, many individuals struggle to manage their personal finances efficiently, often resulting in financial problems such as accumulating debt and the inability to save consistently. This issue is exacerbated by manual financial record-keeping, which is prone to errors and inefficient in providing a comprehensive and real-time financial overview. This study aims to develop and evaluate a personal finance management information system using the Rapid Application Development (RAD) approach. The RAD method was chosen for its iterative and flexible approach, allowing for quick adjustments according to user needs. This study also includes system evaluation through usability testing to measure efficiency, effectiveness, and user satisfaction. The results show that the application of RAD successfully produced high-quality software within three months, featuring key functionalities for managing income and expense data, as well as displaying financial reports based on specific periods. Additionally, usability testing indicated an average score of 87.5%, which falls into the 'Good' category, confirming the system's operational readiness and usability for users.
Model Klasifikasi Risiko Stunting Pada Balita Menggunakan Algoritma CatBoost Classifier Pahlevi, Omar; Wulandari, Dewi Ayu Nur; Rahayu , Luci Kanti; Leidiyana, Henny; Handrianto, Yopi
Bulletin of Computer Science Research Vol. 4 No. 6 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i6.373

Abstract

Stunting is a significant health issue in Indonesia, affecting the growth and development of young children and influenced by various complex risk factors such as nutrition, environment, and access to healthcare services. The manual process of identifying stunting risks often requires considerable time, resources, and specialized expertise from medical professionals. This study aims to develop a stunting risk classification model for young children using machine learning through the CatBoost Classifier algorithm. This algorithm was chosen for its advantages in handling categorical variables without requiring complex encoding processes and its ability to manage imbalanced data, ultimately improving prediction accuracy. In the conducted case study, the model's prediction updates were illustrated by increasing the initial prediction from 0.25 to 0.27 after accounting for residual corrections in the first iteration, with a learning rate of 0.1. This process demonstrates CatBoost's iterative mechanism for improving model predictions through gradual updates. Evaluation results showed that the developed model achieved an accuracy of 98.47% and a ROC-AUC score of 1.00 for several classes, indicating a high capability in accurately classifying stunting risks. These findings suggest that the CatBoost algorithm is effective for stunting risk classification, capable of handling data complexity, and expected to contribute significantly to supporting stunting prevention efforts through improved early detection.
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.
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