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All Journal Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Building of Informatics, Technology and Science Dinasti International Journal of Education Management and Social Science Jurnal Teknologi Dan Sistem Informasi Bisnis Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Journal of Computer System and Informatics (JoSYC) Jurnal Teknik Informatika (JUTIF) Instal : Jurnal Komputer International Journal Of Computer, Network Security and Information System (IJCONSIST) Jurnal Sistem Informasi dan Bisnis Cerdas Ekasakti Pareso Jurnal Akuntansi SmartComp Intechno Journal : Information Technology Journal
Pratama, Irfan
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences

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Klasifikasi Tanaman Anggrek Menggunakan Arsitektur Convolutional Neural Network Berbasis Majority Voting Kurniawan, Muhammad Rifki; Pratama, Irfan
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 14, No 1 (2025): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v14i1.7221

Abstract

Penelitian ini membahas tentang klasifikasi tanaman anggrek menggunakan tiga arsitektur deep learning yang berbeda: Baseline CNN, Xception, dan NASNet Mobile. Berdasarkan analisis, performa dari ketiga model ini dibandingkan menggunakan nilai akurasi dan skor loss. Hasil menunjukkan bahwa NASNet Mobile memiliki performa terbaik dengan akurasi tertinggi dan skor loss terendah. Untuk lebih meningkatkan akurasi dan keandalan prediksi akhir, metode majority voting digunakan untuk menggabungkan hasil prediksi dari ketiga model tersebut. Hasil akhir menunjukkan bahwa dengan menggunakan metode majority voting, akurasi klasifikasi tanaman anggrek mencapai 100%. Penelitian ini menyimpulkan bahwa majority voting dapat secara efektif meningkatkan akurasi klasifikasi dibandingkan dengan menggunakan model tunggal, dengan memanfaatkan keunggulan masing-masing model untuk menghasilkan hasil yang optimal.
Analisis Sentimen Review Aplikasi Jogja Smart Service Pada Google Play Store Menggunakan Metode SVM Kurniawan, Akbar Sidiq; Pratama, Irfan
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 14, No 1 (2025): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v14i1.7303

Abstract

Perkembangan Teknologi Informasi dan Komunikasi (TIK) telah mengubah cara informasi disampaikan dan mempermudah akses informasi bagi masyarakat. Salah satu implementasi smart city adalah layanan publik berbasis TIK, yang bertujuan mengatasi berbagai masalah masyarakat. Dengan informasi TIK terintegrasi, pelayanan publik menjadi lebih efektif, efisien, dan tepat sasaran. salah satunya pelayanan publik adalah aplikasi Jogja Smart Service yang bisa didownload di google play store.  Saat ini aplikasi Jogja Smart Service telah didownload sebanyak 100 ribu kali dengan rating 4.7.  Pada Google play store bisa dilihat di kolom komentar mengenai ulasan yang telah tulis oleh pengguna aplikasi Jogja Smart Service ini. SVM adalah metode yang memberikan hasil yang baik dalam klasifikasi, karena kemampuannya menemukan hyperplane terbaik untuk memisahkan kelas yang berbeda. Dalam penelitian ini, analisis sentimen terhadap ulasan aplikasi Jogja Smart Service dilakukan menggunakan metode SVM dengan variasi kernel: linear, polynomial, RBF, dan Sigmoid. Hasil pengujian menunjukkan bahwa SVM mampu melakukan analisis sentimen ulasan pengguna aplikasi Jogja Smart Service dengan baik, terutama menggunakan RBF kernel yang mencapai akurasi 90%, lebih tinggi dibandingkan dengan linear, polynomial, dan Sigmoid kernel.
Digital transformation of population administration: Enhancing data accessibility in local communities Suria, Ozzi; Prasetyaningrum, Putri Taqwa; Pratama, Irfan
Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Vol. 10 No. 1 (2025): February 2025
Publisher : University of Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/abdimas.v10i1.14983

Abstract

This community service initiative addresses the need for an efficient population administration system in RT3/RW3, Rejowinangun Utara Village, Magelang, where several key issues hinder effective data management. These problems include manual record-keeping with logbooks, which may increase the risk of errors and data loss, limited accessibility to population information, and challenges in adopting digital technology due to limited technical knowledge and infrastructure. To resolve these problems, this activity is conducted in four phases: data gathering, system development, evaluation, and user training. During the data-gathering phase, discussions with the community leader and the collection of family record data were conducted to identify specific needs and challenges. The system development phase focused on creating a user-friendly web-based Population Administration System (PAS) tailored to these requirements. In the evaluation phase, the system was tested and refined based on feedback from the community leader to ensure functionality and usability. The user training phase provided hands-on experience to the community leader, enabling independent use of the system for managing data and generating demographic summaries. The implementation of PAS successfully transformed administrative processes into digital and improved data accessibility for the local community.
Deep Learning Approach for Music Genre Classification using Multi-Feature Audio Representations Asanah, Nurul; Pratama, Irfan
SISTEMASI Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i5.5369

Abstract

Automatic music genre classification is critical for enhancing user experience in streaming platforms and recommendation systems. This study proposes a Convolutional Neural Network (CNN)-based approach using the GTZAN dataset, which contains ten music genres. The original 30-second audio tracks were segmented into overlapping 3-second chunks, then preprocessed and converted into three feature representations: Mel-Spectrogram, Chroma, and Spectral Contrast. CNN model consisting of four convolutional layers with increasing filters (32–256). The model was trained over 13 epochs using the Adam optimizer. The proposed model achieved 91% accuracy, outperforming previous approaches based on single-feature extraction. The integration of diverse spectral and harmonic features enabled the model to better distinguish between similar genres and improved its generalization. This method offers practical value for real-time music classification, automatic tagging, and intelligent audio indexing in music streaming services and digital libraries.
- IoT-Based Attendance System Using Data Storage on Google Spreadsheet and Smart Door Lock In Computer Labs: - Pratama, Irfan; AYUNINGSIH, EKATRI
Bahasa Indonesia Vol 15 No 02 (2023): Instal : Jurnal Komputer Periode (Juli-Desember)
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalkomputer.v15i02.155

Abstract

Before carrying out learning and teaching activities in the computer lab, the thing that needs to be considered is the presence of students. Absence is something that must be done by lecturers and students to find out the number of attendance. The presence of students is used as a parameter for evaluating lecturers and the academic section to determine whether students are allowed to take the midterm and final exams. The output of this study is an IoT-Based attendance system using data storage on Google Spreadsheet and Smart Door Locks in the computer lab. In this study using a 3 x 4 keypad, arduino uno, and jumper cables.The tools used to retrieve attendance data are nodeMCU, Card Reader, Jumper Cable, LCD and Google Spreadsheet as data storage.
Multiclass Classification with Imbalanced Class and Missing Data Pratama, Irfan; Putri Taqwa Prasetyaningrum
IJCONSIST JOURNALS Vol 2 No 1 (2020): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (481.493 KB) | DOI: 10.33005/ijconsist.v2i1.25

Abstract

In any data mining field, the presence of a good shaped data is needed. Yet in the reality, the data condition is far from the expectation as there are possible to have missing values, redundant data, and inconsistent data. There are problems with the dataset to begin with before we overcome the problem of data mining process interpretation. In the raw data level, possible problem such as missing values and data redundancy or inconsistency can be solved by some certain process called preprocessing. On the preprocessing step, the raw dataset is adjusted to the needs of the whole process, one of the adjustments is to handle missing values. Missing values is a certain condition where the expected values of the data are not recorded. The other problems that happen in the real-world dataset especially in categorical data with label or class is the imbalance distribution of the instance for each class. The imbalanced class is a condition where the distribution of the class is skewed or biased. This study emphasizing on the problem solving of missing values and imbalanced class on the dataset. K-NN imputation is a missing value handling method of this study. As for the imbalanced class problem, this study utilizes SMOTE and ADASYN for the comparison. While the dataset will further be tested by various classification methods such as Decision tree, Random Forest, and Stacking. The original dataset produced bad score from the classification process due to the imbalanced data. Then the data undergoing an oversampling process using SMOTE and ADASYN methods in hope that the accuracy will be hugely better. Yet the reality is the accuracy score do not move to the expected number at all with only averaging in 32%-37% of accuracy score in any scheme of process.
Implementation Of Machine Learning To Determine The Best Employees Using Random Forest Method Taqwa Prasetyaningrun, Putri; Pratama, Irfan; Yakobus Chandra, Albert
IJCONSIST JOURNALS Vol 2 No 02 (2021): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (376.533 KB) | DOI: 10.33005/ijconsist.v2i02.43

Abstract

In the world of work the presence of the best employees becomes a benchmark of progress of the company itself. In the determination usually by looking at the performance of the employee e.g. from craft, discipline and also other achievements. The goal is to optimize in decision making to the best employees. Models obtained for employee predictions tested on real data sets provided by IBM analytics, which includes 29 features and about 22005 samples. In this paper we try to build system that predicts employee attribution based on A collection of employee data from kaggle website. We have used four different machines learning algorithms such as KNN (Neighbor K-Nearest), Naïve Bayes, Decision Tree, Random Forest plus two ensemble technique namely stacking and bagging. Results are expressed in terms of classic metrics and algorithms that produce the best result for the available data sets is the Random Forest classifier. It reveals the best withdrawals (0,88) as good as the stacking and bagging method with the same value
Sentiment Analysis of MyBCA Application User Reviews using Naive Bayes, Random Forest, and Decision Tree Akbar, Muhammad Rizky Mawandhyka; Pratama, Irfan
SISTEMASI Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i5.5472

Abstract

In today’s era of globalization, rapid technological advancements are driving innovation across various sectors, including the banking industry. One of the key digital innovations in banking is mobile banking (m-banking), which allows customers to perform transactions via smartphones. This study aims to analyze the sentiment of user reviews on the MyBCA application using three classification methods: Naive Bayes, Random Forest, and Decision Tree. A total of 5,000 user reviews were collected from the Google Play Store through web scraping techniques. The data was preprocessed using the TF-IDF weighting method and processed with Python programming language and the Scikit-Learn library. The dataset was split into 90% training data and 10% testing data. This study also applies the ISO 9126 standard for multi-label classification to assess software quality based on Usability, Efficiency, Functionality, Reliability, and Maintainability. Evaluation results indicate that Random Forest achieved the highest accuracy at 94.09%, outperforming Naive Bayes (81.77%) and Decision Tree (82.38%). This research contributes to the development of a sentiment-based evaluation method for mobile banking applications, integrating user feedback analysis with ISO 9126 quality standards, and offers a useful reference for improving digital banking services.
Comparative Analysis of Gradient Boosting, XGBoost, and KNN on Predicting Student Graduation in Imbalance and Balance Data Schemes Hubu, Muhammad Rizki; Pratama, Irfan
Dinasti International Journal of Education Management And Social Science Vol. 6 No. 6 (2025): Dinasti International Journal of Education Management and Social Science (Augus
Publisher : Dinasti Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/dijemss.v6i6.5362

Abstract

The objective of this research is to compare the performance of three machine learning algorithms: Gradient Boosting, XGBoost, and K-Nearest Neighbors (KNN) in predicting student graduation using a quantitative approach and comparative experimental methods. The analysis process follows the CRISP-DM stages, which include business understanding, data understanding, data preparation, modeling, evaluation, and implementation. The dataset used consists of approximately 1,251 data points from students of the 2018–2020 cohort with an imbalanced distribution, namely 73.78% graduated on time and 6.22% did not graduate on time. The variables analyzed include academic and non-academic data, such as total credits, GPA per semester, number of repeated courses, and number of leaves. To address the data imbalance, the SMOTE-TOMEK balancing technique was applied. The results of this research indicate that XGBoost showed an improvement in performance after balancing, with accuracy, precision, recall, and F1-score reaching 1.0000. Gradient Boosting shows consistent performance with a score of 0.9992, both before and after balancing. KNN also experienced an increase in accuracy from 0.9928 to 0.9968 after the balancing process. Findings from the confusion matrix results show a significant improvement in classification. Therefore, the implementation of the SMOTE-TOMEK technique has proven effective in improving the performance of classification models on imbalanced data, and XGBoost is recommended as the main algorithm for predicting student graduation.
Analisis Perbandingan Algoritma Random Forest dan K-Nearest Neighbors pada Klasifikasi Tingkat Stres Pekerja Manurung, Syalom Kristian; Pratama, Irfan
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7589

Abstract

Work stress has become a prominent concern in the modern professional landscape, as it can lead to reduced productivity, diminished work quality, and decreased mental well-being among employees. This study aims to evaluate and compare the performance of two machine learning algorithms, namely Random Forest and K-Nearest Neighbors (KNN), in classifying levels of work stress. The data were obtained through an online questionnaire completed by 212 respondents from various employment sectors in Indonesia. The responses were converted from Likert scale to numerical values, grouped using the K-Means clustering method, and categorized into five levels of stress, ranging from no stress to very high stress. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The modeling process was conducted using three different data split scenarios, namely 90:10, 80:20, and 70:30, and evaluated using metrics such as accuracy, precision, recall, f1-score, and cross-validation. The findings indicate that the Random Forest algorithm consistently outperformed KNN across all scenarios. After applying SMOTE, both algorithms showed improved performance, with the Balanced Random Forest model achieving the highest accuracy and f1-score of 92 percent in the 70:30 scenario. These results suggest that combining Random Forest with SMOTE offers an effective and reliable solution for classifying work stress levels and could be developed as an objective and efficient early detection system.
Co-Authors Ade Kurnia, Dian Akbar, Muhammad Rizky Mawandhyka Albert Yakobus Chandra Alifudin, Arif Andre Bustari Asanah, Nurul Ayuningsih, Ekatri Dadang Sudrajat Dwi Krisbiantoro, Dwi Edi Wibowo Guno Wibowo, M Ioni Abdurrahman Hubu, Muhammad Rizki Iqbal, Sayid Muhammad Kurniawan, Akbar Sidiq Kurniawan, Muhammad Rifki Manurung, Syalom Kristian Odi Nurdiawan Prasetyaningrum, Putri Taqwa Prayuda, Arya Priatama, Candra Rina Asmeri, Rina Sarmini Sarmini Suria, Ozzi Swapurba, Gangsar Taqwa Prasetyaningrun, Putri Uswatun Hasanah Waluyo, Retno Widianti, Anisa