Article Per Year (5 Year)
p-Index From 2021 - 2026
2.782
P-Index
This Author published in this journals
All Journal Teknosains: Media Informasi Sains dan Teknologi JUSIFO : Jurnal Sistem Informasi Sistemasi: Jurnal Sistem Informasi SMARTICS Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Riset Informatika Jurnal Informatika Global Jurnal Media Informatika Jurnal Sistem Informasi, Teknik Informatika dan Teknologi Pendidikan (JUSTIKPEN) Algoritme Jurnal Mahasiswa Teknik Informatika Jurnal Software Engineering and Computational Intelligence Journal of Intelligent Networks and IoT Global Jurnal Komputer dan Teknik Informatika
Muhammad Haviz Irfani
Universitas Indo Global Mandiri
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Mechanical Engineering Physics Social Sciences Other

Published : 20 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 20 Documents
Search

 1   2 
Analysis Of Public Sentiment Towards The Free Nutritious Meal Program In Schools Based On Tweets Using The K-Nearest Neighbors Method Maharani, Aiga Rizki; Gustriansyah, Rendra; irfani, muhammad haviz
JATISI Vol 12 No 4 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i4.13446

Abstract

The public sentiment analysis of the free nutritious meal program in schools was conducted based on data from the social media platform Twitter (X). This program is an initiative by the Indonesian government aimed at improving the nutritional quality of children, particularly those from underprivileged families, as well as reducing stunting rates. The data used consisted of 3,007 tweets that had undergone preprocessing, manual labeling, and class balancing using oversampling techniques. The K-Nearest Neighbors (K-NN) method was applied to classify sentiment into three categories: positive, negative, and neutral. The data was split with 80% used for training and 20% for testing. The analysis process included data representation using TF-IDF and model evaluation using metrics such as accuracy, precision, recall, and F1-score. Evaluation results showed that the K-NN model with K=3 achieved an accuracy of 82%, with the best performance in classifying negative sentiment tweets (recall = 1.00, F1-score = 0.93). These findings indicate that public opinion toward the program tends to be negative, mainly due to concerns over budget allocation and food distribution. This study is expected to provide input for the government in designing more effective and responsive communication strategies and public policies.
Pengaruh Deteksi Tepi Citra Urat Daun Pada Pengenalan Jenis Bibit Jeruk Menggunakan Metode Pengenalan JST-PB dan GLCM Dimas Apriandi; Gasim; Muhammad Haviz Irfani; Muhammad Ikhwan Jambak
Jurnal Software Engineering and Computational Intelligence Vol 3 No 02 (2025)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v3i02.6219

Abstract

Identifikasi awal jenis bibit jeruk sangat penting untuk menjamin kualitas bibit dan meningkatkan produktivitas pertanian. Identifikasi secara manual membutuhkan keahlian khusus dan rentan terhadap kesalahan. Penelitian ini bertujuan untuk menganalisis pengaruh metode deteksi tepi terhadap akurasi klasifikasi bibit jeruk menggunakan Jaringan Syaraf Tiruan Propagasi Balik (JST-PB) dan fitur tekstur yang diekstraksi dengan Gray Level Co-occurrence Matrix (GLCM). Tiga metode deteksi tepi yaitu Canny, Laplacian of Gaussian (LoG), dan Roberts yang diterapkan pada citra daun jeruk dari empat varietas antara lain: Kunci, Nipis, Purut, dan Sambal. Fitur tekstur berupa contrast, correlation, homogeneity, dan entropy digunakan sebagai masukan dalam pelatihan JST-PB. Hasil penelitian menunjukkan bahwa metode Roberts dengan 30 neuron tersembunyi memberikan kinerja terbaik dengan precision rata-rata 75,56%, recall 75,00%, dan F1-score 74,97%. Hal ini menunjukkan bahwa pemilihan metode deteksi tepi berpengaruh signifikan terhadap akurasi klasifikasi. Kombinasi metode deteksi tepi Roberts, ekstraksi fitur GLCM, dan JST-PB terbukti efektif untuk pengenalan otomatis jenis bibit jeruk berbasis citra digital.    
Pengaruh Splitting Data terhadap Akurasi Klasifikasi Demam Berdarah Dengue Menggunakan K-Nearest Neighbors Ibrahim Akbar, Muhammad; Rudi, Rudy Heriansyah; irfani, muhammad haviz
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 2 (2026): April 2026 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v6i2.13274

Abstract

Early detection of Dengue Hemorrhagic Fever (DHF) is crucial to prevent serious complications and improve treatment effectiveness, particularly in high-case areas such as the Dempo Primary Health Center. This study aims to develop and evaluate a DHF classification system using the K-Nearest Neighbors (K-NN) algorithm with an optimal K value of 5, determined via the Elbow Method. The dataset consists of 200 medical records with an imbalanced class distribution between positive and negative DHF cases. Three data-splitting scenarios (70:30, 80:20, and 90:10) were tested to analyze the effect of data proportion on model performance. Evaluation metrics included accuracy, precision, recall, and F1-score. Results show that the 70:30 scenario achieved the best performance, with 90% accuracy, 96.67% precision, 85.29% recall, and 90.62% F1-score. For comparison, K-NN was tested against Decision Tree and Support Vector Machine (SVM) algorithms as baselines. K-NN demonstrated competitive and more stable performance, with an average accuracy difference of ±2% compared to the other methods. These findings confirm that K-NN provides reliable results for medical data with limited sample size and imbalanced class distribution. This study contributes empirical analysis regarding the influence of varying data split ratios on classification model stability and strengthens the application of machine learning for early DHF detection based on local medical data.
Classification of User Age Based on Mobile Device Usage Value Using Support Vector Machine Method: A Comparative Study of SVM Kernels and Grid Search Optimization Muhammad Amaruna Sahona Sahona; Lastri Widya Astuti; Muhammad Haviz Irfani
Jurnal Media Informatika Vol. 7 No. 3 (2026): Edisi Mei - Juni
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jumin.v7i3.8798

Abstract

Klasifikasi perangkat seluler adalah proses pengelompokan perangkat seperti smartphone berdasarkan karakteristik dan fitur yang dimilikinya. Penelitian ini mengatasi permasalahan kurangnya pemahaman mengenai karakteristik penggunaan perangkat seluler pada berbagai kelompok usia, yang dapat menyebabkan ketidaksesuaian produk dengan kebutuhan pengguna. Tujuan penelitian ini adalah mengklasifikasikan usia pengguna perangkat seluler berdasarkan manfaat perangkat menggunakan metode support vector machine (SVM). Data yang digunakan adalah data sekunder dari dataset platform kaggle, yang mencakup variabel terkait perilaku pengguna, seperti model perangkat, sistem operasi, durasi penggunaan aplikasi, waktu layar menyala, konsumsi daya baterai, jumlah aplikasi terinstal, penggunaan data, dan jenis kelamin pengguna. Pengolahan data melibatkan pembersihan data, ekstraksi fitur, pelatihan model SVM dengan kernel Linear, RBF, Polynomial, dan Sigmoid, serta penyetelan Hyperparameter menggunakan Grid Search. Hasil penelitian menunjukkan bahwa model SVM dengan kernel Linear, Polynomial, dan RBF mencapai akurasi 100%, membuktikan efektivitas metode ini dalam mengklasifikasikan usia pengguna.
Analisis Penerimaan Sistem Informasi Akademik Dengan Menggunakan UTAUT 2 (Studi Kasus: Akademi Keperawatan Pembina Palembang) Siska Anggraini; Muhammad Haviz Irfani; Sri Rahayu
JUSIFO : Jurnal Sistem Informasi Vol 6 No 1 (2020): June
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v6i1.5616

Abstract

Penerimaan terhadap sistem teknologi informasi penting untuk dilakukan karena dapat menjadi indikator bahwa sistem akan diterima dan diterapkan oleh pengguna untuk mendukung proses perkuliahan di Akademi Keperawatan Pembina Palembang. Tujuan dari penelitian ini adalah untuk mengetahui bagaimana pengaruh variabel model Unified Theory of Acceptance and Use of Technology (UTAUT) 2 terhadap penerimaan sistem informasi akademik dan seberapa besar tingkat penerimaan sistem informasi akademik menggunakan model UTAUT 2. Untuk mengetahui penerimaan sistem informasi peneliti menggunakan semua variabel utama dan variabel moderasi umur, jenis kelamin, pengalaman. Data penelitian ini menggunakan 135 responden yaitu 113 sampel mahasiswa dan 22 sampel dosen melalui penyebaran kuesioner. Analisis data yang digunakan yaitumenggunakan metode Structural Equation Modeling (SEM) dengan menggunakan tool Lisrel versi 8.70. Analisis SEM memiliki tahapan yaitu: (1) konseptualisasi model (2) membentuk path diagram (3) identifikasi model (4) estimasi model (5) penilaian model fit (6) menginterpretasikan hasil. Hasil penelitian menunjukan bahwa variabel espektasi kinerja berpengaruh sebesar (16.46), espektasi usaha berpengaruh sebesar (16.54), pengaruh sosial berpengaruh sebesar (14.85), motivasi hedonis berpengaruh sebesar (6.18), nilai harga berpengaruh sebesar (16.59), kebiasaan berpengaruh sebesar (15.94) terhadap niat perilaku. Variabel kondisi fasilitas berpengaruh sebesar (2.22) terhadap perilaku menggunakan. Variabel UTAUT 2 mampu mempengaruhi penerimaan sistem sebesar 9,4%.
Klasifikasi Risiko Burnout Akade Klasifikasi Risiko Burnout Akademik Pada Siswa Kelas XI SMA LTI IGM Berdasarkan Pola Belajar Menggunakan Algoritma Decision Tree: Klasifikasi Risiko Burnout Akademik pada Siswa Kelas XI SMA LTI IGM Berdasarkan Pola Belajar Menggunakan Algoritma Decision Tree Aditya Aji Sagara; Herri Setiawan; Muhammad Haviz Irfani
Jurnal Sistem Informasi, Teknik Informatika dan Teknologi Pendidikan Vol. 6 No. 1 (2026): Edisi September 2026
Publisher : Utiliti Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Burnout akademik merupakan kondisi kelelahan emosional, fisik, dan mental akibat tekanan akademik yang berkepanjangan yang dapat menurunkan motivasi, prestasi belajar, serta kesejahteraan psikologis siswa. Oleh karena itu, diperlukan suatu metode deteksi dini untuk membantu sekolah mengidentifikasi siswa yang berisiko mengalami burnout akademik. Penelitian ini bertujuan mengklasifikasikan tingkat risiko burnout akademik siswa menggunakan algoritma Decision Tree. Penelitian dilakukan terhadap 101 siswa kelas XI SMA LTI IGM dengan menggunakan data hasil kuesioner yang memuat berbagai indikator burnout akademik. Tahapan penelitian meliputi preprocessing data, seleksi fitur menggunakan Information Gain, pembangunan model Decision Tree, penyeimbangan data menggunakan teknik oversampling, serta evaluasi menggunakan confusion matrix, accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa model sebelum proses balancing memperoleh akurasi sebesar 86%, sedangkan setelah dilakukan oversampling performa model meningkat menjadi 94%, dengan nilai precision, recall, dan F1-score masing-masing sebesar 94%. Hasil seleksi fitur juga menunjukkan bahwa indikator keraguan terhadap potensi diri, kecemasan berlebihan, dan kekhawatiran terhadap masa depan merupakan faktor yang paling berpengaruh dalam klasifikasi risiko burnout akademik. Hasil penelitian menunjukkan bahwa penerapan Decision Tree yang dikombinasikan dengan Information Gain dan oversampling mampu meningkatkan performa klasifikasi serta berpotensi digunakan sebagai alat bantu deteksi dini burnout akademik untuk mendukung pengambilan keputusan dan penyusunan intervensi yang lebih tepat di lingkungan sekolah.
INFLUENCE OF LEAF IMAGING DISTANCE ON WATER GUAVA CLASSIFICATION USING NEURAL NETWORK WITH GRAY LEVEL CO-OCCURRENCE MATRIX FEATURES Muhammad Haviz Irfani; Gasim; Andika Afrianto
Jurnal Riset Informatika Vol. 8 No. 1 (2025): Desember 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i1.419

Abstract

The development of Computer Vision technology has made a significant contribution to the agricultural sector, particularly in the identification of plants based on visual characteristics. Water guava (Syzygium aqueum) is one of the fruit commodities widely cultivated in Indonesia; however, its seedling varieties are often difficult to distinguish visually. Conventional methods relying on human observation tend to have low accuracy, highlighting the need for an accurate and efficient identification system from the early stages. This study aims to analyze the effect of varying imaging distances on the extraction results of leaf vein texture features using the Gray Level Co-occurrence Matrix (GLCM) method and to evaluate how this parameter influences the classification performance of water guava seedlings using the Backpropagation Artificial Neural Network (ANN). Unlike previous GLCM–ANN plant classification studies that primarily focused on lighting or species variation, this work systematically investigates imaging distance as a key factor in optimizing texture feature stability and improving model accuracy. Experiments were conducted using five imaging distances—7 cm, 9 cm, 11 cm, 13 cm, and 15 cm—with 2,500 images used for training data and 500 images for testing data. The results show that an imaging distance of 13 cm yielded the best performance, achieving 80% accuracy, where 80 out of 100 test images were correctly classified, supported by balanced precision, recall, and F1-score values indicating stable and reliable classification performance.
Analisis Struktur Tulisan Tangan melalui Deteksi Zona Spasi Antarkata Menggunakan CNN Mendalam Nabilah Mufti; Rudi Heriansyah; Muhammad Haviz Irfani
SMARTICS Journal Vol 12 No 1 (2026): Journal SMARTICS (April 2026)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v12i1.13937

Abstract

Advances in image processing and deep learning technology enable more accurate handwriting analysis, including the detection of interword spacing, which exhibits high complexity due to variations in writing styles. This study aims to implement a Convolutional Neural Network (CNN) algorithm using the You Only Look Once version 11 (YOLOv11) framework to detect and classify interword spacing zones into three classes: Narrow Word Spacing (NWS), Medium Word Spacing (MWS), and Wide Word Spacing (WWS). The dataset comprises 150 handwritten images with a total of 4.117 annotated interword spacing objects. The research methodology involves testing the model across variations of learning rates (0.1, 0.01, 0.001, and 0.0001) and data split ratios (70:30, 80:20, and 90:10). Model performance was evaluated using Precision, Recall, F1-Score, and mean Average Precision (mAP) metrics. Based on 12 experimental trials, the best configuration was achieved with a learning rate of 0.001 and a 90:10 data split. This configuration produced an mAP@50 of 0.455, an mAP@50–95 of 0.261, and an F1-Score of 0.49. These results indicate that the YOLOv11 model is capable of detecting interword spacing zones with reasonably good performance, despite remaining classification errors due to visual similarities between classes.
Implementasi YOLO Framework pada Deteksi Otomatis Sepatu Badminton Pemain di Lapangan Ida Bagus Bisma; Rudi Heriansyah; Muhammad Haviz Irfani
Jurnal Komputer dan Teknik Informatika Vol. 1 No. 2 (2026): Edisi: Februari-April
Publisher : Pustaka Bangsa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penggunaan sepatu yang tidak sesuai saat bermain badminton dapat meningkatkan risiko cedera serta merusak flooring lapangan. Oleh karena itu, diperlukan sistem deteksi otomatis untuk mengidentifikasi jenis sepatu yang digunakan pemain guna meningkatkan kepatuhan terhadap peraturan lapangan. Penelitian ini mengusulkan sistem deteksi sepatu badminton menggunakan algoritma Convolutional Neural Network (CNN) dengan framework You Only Look Once (YOLO). Dataset yang digunakan terdiri dari tiga kelas, yaitu Badminton Shoes, Warning Shoes, dan Foot, yang diperoleh dari situs resmi produk sepatu serta platform Roboflow. Proses pelatihan model meliputi tahap anotasi, preprocessing, dan augmentasi data. Evaluasi kinerja model dilakukan menggunakan confusion matrix dengan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa model mampu mendeteksi dan mengklasifikasikan jenis sepatu dengan tingkat akurasi yang tinggi. Sistem ini berpotensi membantu pengelola lapangan dalam melakukan pengawasan otomatis serta mengurangi risiko cedera akibat penggunaan sepatu yang tidak sesuai.
Prediksi Saham IHSG Menggunakan Extreme Gradient Boosting, Support Vector Machine, K-Nearest Neighbors Dan Autoregressive Integrated Moving Average Renaldy Pratama; Shinta Puspasari; Muhammad Haviz Irfani
Jurnal Software Engineering and Computational Intelligence Vol 4 No 01 (2026)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v4i01.7141

Abstract

The Jakarta Composite Index (JCI) is a key indicator in assessing the performance of the Indonesian capital market. Dynamic stock price fluctuations require accurate prediction methods to assist investors in decision making. This study aims to compare the performance of four prediction algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Extreme Gradient Boosting (XGBoost), and Autoregressive Integrated Moving Average (ARIMA) in predicting the closing price of JCI. The data used is the JCI daily historical data for the 2019-2024 period obtained from Yahoo Finance. The research process includes data pre-processing, prediction model implementation, model training and testing, and performance evaluation using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics on a 0-1 normalization scale.The results showed that the ARIMA model provided the most stable results with an RMSE value of 0.4038 and MAE of 0.3945, followed by K-NN and SVM. Although SVM has the lowest MAE value, its RMSE is still higher than ARIMA and K-NN. SVM showed the lowest performance in this experiment. Based on the evaluation results, ARIMA is recommended as the best algorithm in predicting JCI closing price based on historical data. The Jakarta Composite Index (JCI) is a key indicator in assessing the performance of the Indonesian capital market. Dynamic stock price fluctuations require accurate prediction methods to assist investors in decision making. This study aims to compare the performance of four prediction algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Extreme Gradient Boosting (XGBoost), and Autoregressive Integrated Moving Average (ARIMA) in predicting the closing price of JCI. The data used is the JCI daily historical data for the 2019-2024 period obtained from Yahoo Finance. The research process includes data pre-processing, prediction model implementation, model training and testing, and performance evaluation using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics on a 0-1 normalization scale.The results showed that the ARIMA model provided the most stable results with an RMSE value of 0.4038 and MAE of 0.3945, followed by K-NN and SVM. Although SVM has the lowest MAE value, its RMSE is still higher than ARIMA and K-NN. SVM showed the lowest performance in this experiment. Based on the evaluation results, ARIMA is recommended as the best algorithm in predicting JCI closing price based on historical data.
Co-Authors Aditya Aji Sagara Andika Afrianto Daniel Udjulawa DEWI SARTIKA Dimas Apriandi Gasim Gasim gasim, Gasim Herri Setiawan Ibrahim Akbar, Muhammad Ida Bagus Bisma Imelda Saluza, Imelda Juhaini Alie Lastri Widya Astuti Maharani, Aiga Rizki Muhammad Amaruna Sahona Sahona Muhammad Ikhwan Jambak Muhammad Muhardeny Nabilah Mufti Nazori Suhandi Ramadhani, Kiki Renaldy Pratama Rendra Gustriansyah Rudi Heriansyah Rudi, Rudy Heriansyah Sartika, Dewi Shinta Puspasari Siska Anggraini Sri Rahayu