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All Journal Jurnal Informatika dan Teknik Elektro Terapan Register: Jurnal Ilmiah Teknologi Sistem Informasi Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Building of Informatics, Technology and Science Jurnal Teknologi Dan Sistem Informasi Bisnis Journal of Computer System and Informatics (JoSYC) Jurnal Teknik Informatika (JUTIF) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Instal : Jurnal Komputer International Journal Of Computer, Network Security and Information System (IJCONSIST) Kaunia: Integration and Interconnection of Islam and Science Journal Jurnal Sains dan Teknologi Jurnal Sistem Informasi dan Bisnis Cerdas Ekasakti Pareso Jurnal Akuntansi SmartComp Edu Komputika Journal
Pratama, Irfan
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- 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
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.
Hybrid Model for Speech Emotion Recognition using Mel-Frequency Cepstral Coefficients and Machine Learning Algorithms Nurdiawan, Odi; Ade Kurnia, Dian; Sudrajat, Dadang; Pratama, Irfan
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5143

Abstract

Speech Emotion Recognition (SER) is a subfield of affective computing that focuses on identifying human emotions through voice signals. Accurate emotion classification is essential for developing intelligent systems capable of interacting naturally with users. However, challenges such as background noise, overlapping emotional features, and speaker variability often reduce model performance. This study aims to develop a lightweight hybrid SER model by combining Mel-Frequency Cepstral Coefficients (MFCC) as feature representations with three machine learning algorithms: Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbors (KNN). The methodology involves audio data preprocessing, MFCC-based feature extraction, and classification using the selected algorithms. The RAVDESS dataset, consisting of 1,440 English-language audio samples across four emotions (happy, angry, sad, neutral), was used with an 80/20 train-test split to ensure class balance.. Experimental results show that the KNN model achieved the highest performance, with an accuracy of 78.26%, precision of 85.09%, recall of 78.26%, and F1-score of 77.06%. The Decision Tree model produced comparable results, while the SVM model performed poorly across all metrics. These findings demonstrate that the proposed hybrid approach is effective for recognizing emotions in speech and offers a computationally efficient alternative to deep learning models. The integration of MFCC features with multiple machine learning classifiers provides a robust framework for real-time emotion recognition applications, especially in environments with limited computing resources.
Measuring Resampling Methods on Imbalanced Educational Dataset’s Classification Performance Pratama, Irfan; Prasetyaningrum, Putri Taqwa; Chandra, Albert Yakobus; Suria, Ozzi
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 10 No 1 (2024): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v10i1.3397

Abstract

Imbalanced data refers to a condition that there is a different size of samples between one class with another class(es). It made the term “majority” class that represents the class with more instances number on the dataset and “minority” classes that represent the class with fewer instances number on the dataset. Under the target of educational data mining which demands accurate measurement of the student’s performance analysis, data mining requires an appropriate dataset to produce good accuracy. This study aims to measure the resampling method’s performance through the classification process on the student’s performance dataset, which is also a multi-class dataset. Thus, this study also measures how the method performs on a multi-class classification problem. Utilizing four public educational datasets, which consist of the result of an educational process, this study aims to get a better picture of which resampling methods are suitable for that kind of dataset. This research uses more than twenty resampling methods from the SMOTE variants library. as a comparison; this study implements nine classification methods to measure the performance of the resampled data with the non-resampled data. According to the results, SMOTE-ENN is generally the better resampling method since it produces a 0,97 F1 score under the Stacking classification method and the highest among others. However, the resampling method performs relatively low on the dataset with wider label variations. The future work of this study is to dig deeper into why the resampling method cannot handle the enormous class variation since the F1 score on the student dataset is lower than the other dataset.
Subduction and Local Fault Earthquake Analysis Using ST-DBSCAN Clustering Algorithm in The Special Region of Yogyakarta (DIY) Handayani, Wuri; Pratama, Irfan; Wibowo, Nugroho Budi
Kaunia: Integration and Interconnection Islam and Science Journal Vol. 21 No. 1 (2025)
Publisher : Fakultas Sains dan Teknologi UIN Sunan Kalijaga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/kaunia.5347

Abstract

This study aims to analyze the spatio-temporal patterns of subduction and local fault earthquakes in the Special Region of Yogyakarta using the ST-DBSCAN (Spatio-Temporal Density-Based Spatial Clustering of Applications with Noise) algorithm. A total of 5,403 earthquake events from 2019 to 2024 were clustered using spatial parameters (2–5 km) and a temporal window of 10 days. The results were evaluated using the Davies-Bouldin Index (DBI) and Silhouette Score. In the subduction zone, nine clusters were identified with a DBI of 3.23 and a Silhouette Score of 0.18, indicating moderate separation. Meanwhile, 25 clusters were detected in the local fault zone, particularly around the Opak and Oyo Faults, with a higher DBI of 3.82 and a negative Silhouette Score (-0.14), suggesting overlapping clusters and weak structure. The clustering outcomes correlate with geological features and offer insights for improving earthquake hazard assessment and early warning systems in Yogyakarta.
Implementasi Sistem Pendukung Keputusan Rekomendasi Pemasok Kayu Furniture Dengan Menggunakan Metode Smart (Studi Kasus : Mebel Cempaka Jaya) Paneo, Anto Farwanto; Pratama, Irfan
Jurnal Sains dan Teknologi (JSIT) Vol. 3 No. 3 (2023): September - Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v3i2.973

Abstract

Pada era saat ini teknologi sudah semakin berkembang, hampir seluruh kegiatan manusia yang dikerjakan secara manual sudah semakin berkurang dan banyak di tangani dengan teknologi terkhususnya di bidang usaha mebel, pada saat ini berbisnis dapat dilakukan dengan memanfaatkan teknologi, namun pada era yang canggih seperti ini Mebel Cempaka Jaya masih menggunakan cara manual dalam menentukan pemasok kayu furniture terbaik, Permasalahan pada Mebel Cempaka jaya sekarang mereka mengalami kesulitan dalam menentukan supplier kayu yang baik untuk dijadikan bahan pembuatan furniture. Tujuan yang ingin diselesaikan dalam penelitian ini yaitu agar owner maupun pemilik mebel cempaka jaya dapat menemukan beberapa rekomendasi pemasok kayu furniture yang sesuai dengan mebel tersebut dan sesuai standarisasi yang diinginkan. Metode yang digunakan dalam penelitian ini yaitu metode SMART (Simple Multi Attribute Rating Technique) dimana pengambilan keputusan ini menangani permasalahan multi-kriteria berdasarkan pada nilai-nilai yang dimiliki oleh setiap alternatif pada masing-masing kriteria yang telah diberi bobot. Bobot setiap kriteria digunakan untuk membandingkan antara tingkat kepentingan antara kriteria satu dengan yang lain. Pada Pembahasan akan menggunakan 4 kriteria yang telah di tetapkan agar bisa dilakukannya perhitungan dalam pengambilan keputusan, kriteria yang di tentukan yaitu mulai dari Harga, Pengiriman, Kualitas, dan juga Customer Service maupun Pelayanan, Pada perhitungan manual data yang di ambil adalah 6 data sebagai sample dari 12 data pemasok sebagai alternatif. Pada penelitian yang sudah dilakukan, maka di daperoleh kesimpulan bahwasanya dari sebanyak 12 alternatif. Hasil yang menjadi rekomendasi untuk mebel cempaka jaya adalah Toko Palgam dengan nilai 0,86, Toko Flamingo dengan Nilai 0,83, dan Toko Dwi Restu dengan nilai 0,77.
PENJADWALAN MASA TANAM PADI DAN JAGUNG BERDASARKAN HASIL PREDIKSI CURAH HUJAN MENGGUNAKAN ARIMA DI WILAYAH SLEMAN Pratama, George Recksy Sandy; Pratama, Irfan
Jurnal Informatika dan Teknik Elektro Terapan Vol. 11 No. 3s1 (2023)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v11i3s1.3375

Abstract

Kabupaten Sleman didukung oleh irigasi teknis dan sebagian besar wilayahnya merupakan lahan pertanian. Iklim tropis dan kelembaban yang tinggi akan berdampak pada produksi pertanian di beberapa daerah. Untuk mendukung produksi pertanian di wilayah Sleman, hasil prediksi curah hujan digunakan untuk menentukan penjadwalan tanam yang tepat. Untuk memprediksi curah hujan dilakukan dengan metode ARIMA, karena data curah hujan berasal dari himpunan waktu yang tidak stasioner, ARIMA digunakan untuk menghimpun waktu yang tidak stasioner. Dengan model ARIMA dalam memprediksi curah hujan dan mendapatkan penjadwalan musim tanam pertanian, harus ditentukan nilai minimum AIC (Akaike Information Criterion) dan BIC (Bayesian Information Criterion) yang ditentukan dari beberapa model ARIMA yang digunakan. Kemudian menghitung nilai RMSE dan MAPE hasil perhitungan presisi. Petani dapat mempersiapkan perubahan curah hujan dalam produksi pertanian dengan prediksi curah hujan di masa mendatang. Selain itu, berdasarkan hasil prediksi curah hujan wilayah Sleman dapat membantu menentukan penjadwal tanam tanaman yang tepat.
Klasifikasi Kanker Payudara Berdasarkan Gambar Histopatologi Menggunakan Metode Convolutional Neural Network Dengan Arsitektur VGG-16 Nandasari, Dayang; Pratama, Irfan
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7377

Abstract

Breast cancer is one of the deadliest diseases with a high prevalence worldwide, especially in women. Breast cancer is the third leading cause of death in Indonesia. Based on Globocan Center data, there will be approximately 408,661 new cases and nearly 242,099 deaths in Indonesia by 2022. Early detection through histopathology images is very important to increase the patient's chances of recovery. However, the diagnosis process carried out manually by pathologists is quite time consuming and affects subjectivity. This study aims to develop a histopathology image-based breast cancer classification system using VGG-16. The dataset to be used consists of histopathology images that are grouped into 2 classes, namely benign and malignant. The data went through several preprocessing stages, including splitting and augmentation, to improve data quality. Test results show that this model achieves 91% accuracy, along with high precision, recall, and F1-scores on the test data. The performance of this model compares favorably with ensemble architectures such as, MobileNet, MobileNetV2. These findings indicate that the proposed approach can be an effective solution as a histopathology image-based breast cancer diagnosis tool.
Co-Authors Ade Kurnia, Dian Albert Yakobus Chandra Alifudin, Arif Amymaztura, Rezky Andre Bustari Asanah, Nurul Aulia, Indira Dhekawanti 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 Nandasari, Dayang Nugroho Budi Wibowo Odi Nurdiawan Paneo, Anto Farwanto Pradana, Michael Anggun Kado Prasetyaningrum, Putri Taqwa Pratama, George Recksy Sandy Priatama, Candra Rina Asmeri, Rina Sarmini Sarmini Suria, Ozzi Taqwa Prasetyaningrun, Putri Uswatun Hasanah Waluyo, Retno Wuri Handayani