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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON)
Abd. Charis Fauzan
Universitas Nahdlatul Ulama Blitar, Blitar
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Implementasi Algoritma K-Medoids Dengan Evaluasi Davies-Bouldin-Index Untuk Klasterisasi Harapan Hidup Pasca Operasi Pada Pasien Penderita Kanker Paru-Paru Ike Wahyu Septiani; Abd. Charis Fauzan; Muhamat Maariful Huda
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 3 No. 4 (2022): Juni 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v3i4.4055

Abstract

Lung Cancer is a disease in which there are cells that grow in the lungs by a collection of carcionogens uncontrollably. Lung Cancer can be treated with surgery, chemotherapy and radiotherapy. Early treatment that needs to be done to reduce the mortality rate in patients with lung cancer after performing thoracic surgery, by collecting data from each patients regarding this information causes a new problem, including the data obtained including high-dimensional data and has many attributes so that it can produce less accurate information. So it is necessary to calculate data mining clustering. In general, the methods for performing clustering are grouped into four parts, namely partitioning, hierarchial, grid-based and model-based. This study used the k-medoids algorithm because it is able to handle data sensitive to outliers and has high accuracy and efficiency in processing large numbers objects. The results of the k-medoids calculation were evaluated using the euclidean distance Davies Bouldin Index which resulted in a DBI value of 0.93543 indicating that the k-medoids algoritm achieves good grouping because the final result of the calculation is less than 0. From the results of the evaluation using DBI it shows that the k-medoids algorithm has an average accumulation average at the time of execution is quite fast and the cluster quality is good. 
Komparasi Jarak Euclidean dan Manhattan Pada Algoritma K-Nearest Neighbor Dalam Mendeteksi Penyakit Diabetes Mellitus Agustin Ely Rahayu; Abd. Charis Fauzan; Harliana Harliana
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 2 (2022): Desember 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5046

Abstract

Diabetes Mellitus is a chronic disease. This disease is caused by an increase in blood sugar levels in the body, it can cause diseases such as heart disease, obesity, and eye, kidney, and nerve diseases. Detection of Diabetes Mellitus is usually carried out by laboratory tests, so that patients have to undergo several medical tests to provide input values to a computerized diagnostic system which has proven to be expensive and has long queue times. From these problems, an artificial intelligence system is needed to diagnose this disease more easily and quickly. Therefore, the researcher aims to use an intelligent system to produce the highest accuracy from the results of the classification test using the K-Nearest Neighbor (K-NN) method with Euclidean distance and Manhattan distance. The class classifications used were pregnancy calculations, blood sugar in blood, blood pressure, skin fold thickness, insulin, body weight, diabetes genealogy dysfunction, and age. The research data in the form of datasets amounted to 450 datasets and the data was divided into two to determine the highest accuracy of 80% test data and 20% for training data. The highest accuracy using Euclidean distance is 84% with a value of K=5, and secondly, the Manhattan distance has the highest accuracy of 82% with a value of K=7.
Penerapan Metode Weighted Product Berbasis Visualisasi Graph Database dalam Merekomendasikan Parfum Isi Ulang Defy Lukbatul Qolbiah; Abd. Charis Fauzan; Tito Prabowo
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 4 (2023): Juni 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6181

Abstract

Perfume is useful for increasing self-confidence, creating satisfaction, eliminating bad odors, and making self-assessment more attractive. Refill perfumes are made from certain perfume seeds dissolved in a suitable solvent. Perfume has many types and strengths of aroma, but there are obstacles when people want to choose the desired perfume scent. This problem becomes research material because it is expected that this problem can be solved. To determine perfume recommendations, it is calculated using the Weighted Product method and visualized using a graph database. In the Neo4j Graph Database visualization, the perfume category and perfume name are used as nodes and the ranking results are used as edges. From the ranking results using the Weighted Product method, 21 perfumes for each category are entered into the Graph Database visualization and a total of 63 perfumes will appear in the perfume recommendation system.Refill perfume is a perfume made from certain perfume seeds dissolved in the appropriate solvent.
Analisis Perbandingan Kinerja Decision Tree C4.5 dan ID3 dalam Klasifikasi Kemiskinan Masyarakat Rahma Garwati; Muhamat Maariful Hudha; Abd. Charis Fauzan
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.7939

Abstract

Thiis study aiims to compare the performanice of two deciision tree algoriithms, namely ID3 and Ci4.5, in classifying poverty status using household survey data from 2024. A quantitative experimental approach was employed, involving stages such as feature selection, data cleaning, transformation, model training, and performance evaluation. The ID3 algorithm was implemented manually based on eintropy iand information gaiin, while thei C4.5 algoriithm was implemented using thei scikit-learn library with parameter adjustments, including limiting tree depth and minimum samples per split to reduce the risk of overfitting. The idataset was divided iinto 70i% training and 30i% testing data. Performance evaluatiion used metriics including accuracy, precisiion, recalli, Fi1-score, and confusion matrix analysis. Results indicate that both algorithms can effectively classify poverty status, but the ID3 algorithm outperforms C4.5 in all evaluation metrics. However, C4.5 provides better model stability and a more simplified tree structure. Thesei fiindingis isuggest that the choiice of algoriithm should be taiilored to ithe dataset characteristics and analytical needs. This study contributes to the appliication of data miining techniiques in supporting poverty analysis and data-driven policy decisions, ensuring a more accurate and targeted distribution of social assistance.
Co-Authors Agustin Ely Rahayu Defy Lukbatul Qolbiah Harliana Harliana Ike Wahyu Septiani Muhamat Maariful Huda Muhamat Maariful Hudha Rahma Garwati Tito Prabowo