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All Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Impression : Jurnal Teknologi dan Informasi
Ari Rizkita
Universitas Efarina
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering

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Akurasi K-Means dengan Menggunakan Cluster dan Titik Grid Terbaik pada Pemetaan Grid Interatif K-Means Johanes Terang Kita Perangin Angin; Ari Rizkita; Robet Robet; Octara Pribadi
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 1 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No1.pp127-129

Abstract

Traditional K-Means face 2 (two) main problems, namely: Determination of Initial Centroid and poor initial cluster. Determining the initial centroid using random numbers is one of the main problems in classical K-Means which results in low accuracy and long computation time. Likewise, determining the good centroid of each cluster without being accompanied by a process of paying attention to the performance of each cluster can also cause the accuracy value obtained is not good. This study will contribute to how the performance obtained by determining a good initial centroid is combined with the use of a good cluster. Determination of a good initial centroid is done by using the K-Means Grid Mapping which divides the determination of the centroid into several Grid Points. The result of this research is a combination of Iterative K-Means with Grid Mapping K-Means to become Iterative Grid Mapping K-Means which will get a good initial centroid and also a good cluster shown in the table of iris and abalone, comparison of the variables in the iris and abalone affecting the best cluster as a result.
Klasifikasi Depresi Menggunakan Analisis Algoritma Random Forest Pada Data Kesehatan Mental Iqbal Giffari Ritonga; Hartono; Syaipuddin Syaipuddin; Ari Rizkita
Impression : Jurnal Teknologi dan Informasi Vol. 5 No. 2 (2026): Juli 2026
Publisher : Lembaga Riset Ilmiah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59086/jti.v5i2.1955

Abstract

Depresi merupakan gangguan kesehatan mental yang dapat menurunkan kualitas hidup dan produktivitas, sehingga deteksi dini diperlukan untuk mendukung pencegahan dan penanganan. Penelitian ini bertujuan mengklasifikasikan depresi menggunakan algoritma Random Forest berdasarkan faktor demografis, gaya hidup, dan kesehatan mental. Dataset terdiri dari 502 data dengan 11 atribut yang dibagi menjadi 401 data latih dan 101 data uji menggunakan train-test split 80:20. Kebaruan penelitian terletak pada analisis feature importance untuk mengidentifikasi faktor dominan dalam prediksi depresi. Hasil menunjukkan Random Forest mencapai akurasi 91,09% dengan precision, recall, dan F1-score yang tinggi pada kedua kelas. Academic Pressure menjadi fitur paling dominan, diikuti suicidal thoughts, usia, jam belajar, kepuasan belajar, dan stres finansial. Confusion matrix juga menunjukkan kesalahan klasifikasi yang rendah, dengan dua kasus false negative. Hasil ini menunjukkan bahwa Random Forest berpotensi mendukung pengembangan sistem deteksi dini depresi berbasis machine learning.   Depression is a mental health disorder that can reduce quality of life and productivity, making early detection important for prevention and intervention. This study aims to classify depression using the Random Forest algorithm based on demographic, lifestyle, and mental health factors. The dataset consisted of 502 records with 11 attributes, divided into 401 training and 101 testing data using an 80:20 train-test split. The novelty lies in applying feature importance analysis to identify dominant factors in depression prediction. The results show that Random Forest achieved 91.09% accuracy, with high precision, recall, and F1-score for both classes. Academic Pressure was the most influential feature, followed by suicidal thoughts, age, study hours, learning satisfaction, and financial stress. The confusion matrix also indicated low classification errors, with only two false-negative cases. These findings indicate that Random Forest has potential to support the development of machine learning-based early depression detection systems.
Co-Authors Hartono Iqbal Giffari Ritonga Johanes Terang Kita Perangin Angin Octara Pribadi Robet Robet Syaipuddin Syaipuddin