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All Journal INTEK: Informatika dan Teknologi Informasi JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) QOSIM: Jurnal Pendidikan, Sosial & Humaniora Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen International Journal of Artificial Intelligence in Medical Issues
Yuli Praptomo Pamungkas Hari Sungkowo
Program Studi Teknik Informatika STMIK El Rahma Yogyakarta
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Engineering Health Professions Medicine & Pharmacology Public Health Social Sciences Other

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Klasifikasi Kesiapan Anak Taman Kanak-Kanak Masuk Sekolah Dasar Menggunakan Metode Naive Bayes Yuli Praptomo Pamungkas Hari Sungkowo; Deni Ambarwati
INTEK : Jurnal Informatika dan Teknologi Informasi Vol. 6 No. 1 (2023)
Publisher : Universitas Muhammadiyah Purworejo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37729/intek.v6i1.3037

Abstract

In accordance with government regulations that prospective new students in grade 1 (one) elementary school must meet the age requirements of 7 (seven) years; or at least 6 (six) years on July 1 of the current year. In addition to the age factor, there are other aspects that affect the readiness of kindergarten children to enter elementary school, namely cognitive, gross motor, fine motor, language, and social emotional aspects. However, many students enter elementary school although there are still some aspects that cannot be fulfilled. Therefore, a classification is needed to find out whether these students have readiness to enter the elementary school level or not. The classification of children's readiness to enter elementary school in this study uses the nave Bayes method, with attributes used include age, ability to read, write, count, and independence. The results of this study are to determine the readiness of students to enter elementary school so that later students and teachers will not be overwhelmed in participating in teaching and learning activities. Based on the test results, obtained an accuracy value of 94.23%. From the test results, it can be concluded that the Naïve Bayes method can be used to classify students' readiness to enter elementary school.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN GURU BARU PADA TK WALADUN SHOLIHUN MENGUNAKAN METODE TOPSIS Yuli Praptomo PHS; Siti Rahayu
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 19 No 3 (2021): September 2021
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v19i3.52

Abstract

The process of selecting new teacher recruitment at Tk Waladu Sholihun generally still uses a manual system, where the determination is based on an assessment of 5 missing criteria, using a computerized system, resulting in inaccurate or less maximal problems arising by the school and in selecting recruitment new teacher.The system that will be used to perform analysis and research is using the TOPSIS method. The criteria used in determining decision making are work experience, ability test, interview test, latest education, age. The Decision Support System determines the acceptance of this new teacher based on the desktop using the PHP programming language and the MySQL database as data storage.This system produces the best choice of teacher selection from the alternative options produced. The final result of the TOPSIS method produces the best alternative choice that is determined. This system makes the process of determining the selection of new teacher recruitment computerized
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN SANTRI BARU TERAS TAHFIDZ DI TERAS DAKWAH MENGGUNAKAN METODE TOPSIS Yuli Praptomo PHS; Selfia Destiana
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 18 No 3 (2020): September 2020
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v18i3.63

Abstract

This research was made to build a decision support system application for selecting new prospective students in the Teras Tahfidz. The methodology used in the decision support system process uses the TOPSIS (Technique For Others Reference by Similarity to Ideal Solution) method. It is hoped that this research can improve the quality of students enrolled later, and can make time efficient in selecting students. By using the Topsis Method, in this study a case will be raised, namely finding the best alternative based on predetermined criteria using the Topsis method. This method was chosen because it is able to select the best alternative. A number of alternatives, in this case the intended alternatives are new students of Teras Tahfidz who register based on specified criteria. The study was conducted by finding the value of the level of importance for each attribute, then carried out the process.
SISTEM PENDUKUNG KEPUTUSAN SELEKSI CALON PENERIMA BEASISWA DI SEKOLAH TINGGI PENDIDIKAN ISLAM BINA INSAN MULIA YOGYAKARTA MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING (SAW) Yuli Praptomo PHS; Edi Sastrawijoyo
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 18 No 2 (2020): Mei 2020
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v18i2.68

Abstract

Bina Insan Mulia Yogyakarta College of Islamic Education offers a scholarship program for underprivileged and outstanding students. To determine the prospective scholarship recipients, the Islamic Education College of Bina Insan Mulia Yogyakarta still uses the manual method by comparing the criteria data one by one that must be adjusted to the predetermined criteria, this causes the selection process to experience difficulties and data errors due to the number of registrants many. This research aims to build a Scholarship Recipient Selection Decision Support System by applying the Simple Additive Weighting (SAW) method in it. The research method used is the study of literature which aims to study and understand the basic theories of decision support systems and the theory of Simple Additive Weighting (SAW). To find out the problem under study is the interview method, to find out information data relating to the problem under study is a method of collecting data and to study documents related to the object of research is the method of documentation. The results of testing the performance of the Decision Support System application using the Simple Additive Weighting (SAW) method stated that this system is able to provide a ranking of scholarship recipients' recommendations in accordance with the criteria used. The conclusion of this study is that this system was built to help the Islamic Education College of Bina Insan Mulia Yogyakarta to select prospective scholarship recipients. The method applied in this system is able to provide a solution to the problem of selection of prospective scholarship recipients with a ranking model that matches the applicable criteria and the weights of those criteria.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN SANTRI TELADAN PONDOK PESANTREN AL MUNAWWIR KOMPLEK NURUSSALAM DENGAN METODE ANALYTICAL HIERARCHY PROCESS (AHP) BERBASIS PHP Yuli Praptomo PHS; Moh Sidik Warsono
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 17 No 3 (2019): September 2019
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v17i3.74

Abstract

Al Munawwir Islamic Boarding School, Nurussalam Complex, is still considered a non- modern cottage, all types of calculation, inputting, analysis can still be said to have not been a computerized system. So to realize a modern boarding school there must be a system that can change the work to be more practical, easy, fast and correct. The Al Munawwir Islamic Boarding School Nurussalam Complex holds an Exemplary Santri Election every year. Because the existence of a model student santri election was made to produce inaccurate data, because all data retrieved was not based on a correct data. The results of the research conducted is the application program for the Santri Decision Support System modeled by the AHP Method. This Decision Support System Application is based on PHP. The application of this Decision Support System is only for helpers to make more accurate and accurate model student examples with data taken correctly
SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN LOKASI PENDIRIAN HOTEL BARU BERBASIS ANALYTICAL HIERARCHY PROCESS (AHP) DI KABUPATEN KULON PROGO Yuli Praptomo PHS
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 16 No 3 (2018): September 2018
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v16i3.90

Abstract

Decision Support Systems or Decision Support Systems (DSS) are defined as interactive computer-based systems, which help decision makers to use data and models to solve unstructured problems. Decision Support Systems are interactive computer systems that can provide alternative solutions for decision makers, so decisions can be made more quickly and accurately. In an effort to speed up the decision-making process and avoid the subjectivity of the resulting decisions a decision support system is needed that aims to assist job management as decision makers in deciding the right location to establish new hotels through the selection process using the Analytical Hierarchy Process (AHP) model. This system is expected to assist management in comparing value criteria, applying pairwise comparison matrices to get the weight of each criterion both local and global criteria. The AHP method is chosen because this method can carry out multi-objective measurements and several criteria, according to the requirements in the decision for the establishment of new hotels that rely on criteria.
MANAJEMEN USER DENGAN SISTEM LIMITASI QUOTA DAN LIMITASI BANDWIDTH PADA ROUTER MIKROTIK Yuli Praptomo PHS; M. Insan Nur Firdaus
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 17 No 2 (2019): Mei 2019
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v17i2.94

Abstract

The internet has played a major role in the dissemination of information and communication so that the wider range of benefits for various groups of users is also widespread, including the PT. Wahana Lintas Nusa Persada. However, the internet connection is not regulated, either the speed of access or management of the user can cause the potential to be used improperly. There is no limitation or limitation of access speed, data usage quota and user settings at PT. Wahana Lintas Nusa Persada is a problem that needs to be solved. These problems can be overcome by managing the user as well as limiting bandwidth and quota data using Mikrotik. With User Manager on Mikrotik, user management and bandwidth and quota data settings can be done. This implementation is carried out in several stages, namely user analysis and access needs of each user to determine the limits of both speed and data quota, proper and efficient system design in implementation and system testing carried out by testing methods. Based on the implementation that has been carried out, it can facilitate user management and distribution of internet connections along with the speed of access and data quota according to the needs of employees and the efficiency of their use during working hours at PT. Wahana Lintas Nusa Persada.
Confidence-Aware Depression Severity Detection in Low-Resource Urdu Social Media Text: A Multilingual Machine Learning Approach Ahmad Naswin; Yuli Praptomo Pamungkas Hari Sungkowo; Lukman Syafie
International Journal of Artificial Intelligence in Medical Issues Vol. 4 No. 1 (2026): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v4i1.434

Abstract

Depression is a major mental health concern that requires early identification and timely intervention. Social media has become an important source of user-generated text that may reflect emotional distress, hopelessness, social withdrawal, and suicidal ideation. However, most existing depression detection studies focus on English or high-resource languages, while research on low-resource languages such as Urdu remains limited. This study investigates depression severity classification in Urdu social media text using multilingual and confidence-aware natural language processing approaches. The dataset consists of 4,000 Twitter/X posts collected between January 2024 and April 2025, annotated into four severity classes: none, mild, moderate, and severe. Each post is represented in three parallel textual forms: native Urdu script, Roman Urdu transliteration, and English translation. The dataset also includes label confidence scores, human verification indicators, cultural markers, and depression-related keywords. Several text representation scenarios were evaluated, including Urdu text, Roman Urdu text, English text, and combined multilingual features. Baseline machine learning models were developed using TF-IDF features with Logistic Regression, Linear Support Vector Machine, and Multinomial Naive Bayes. Confidence-aware learning was examined by incorporating label confidence scores as sample weights and by evaluating a high-confidence subset. The experimental results showed that all baseline models achieved perfect classification performance, with accuracy, macro F1-score, weighted F1-score, and Cohen’s Kappa values of 1.000 across the evaluated scenarios. These results indicate that the dataset contains highly separable linguistic patterns among depression severity classes. However, further inspection suggests that repeated or highly similar textual patterns may contribute to overly optimistic performance. Therefore, stricter validation using duplicate-free splitting, external datasets, and transformer-based models is recommended for future work. This study provides a preliminary benchmark for multilingual depression severity classification in low-resource Urdu text and highlights the potential of AI-driven mental health informatics as a supportive early-warning tool rather than a clinical diagnostic system
Analisis Sentimen Ulasan Berbahasa Inggris Apex Legends di Steam Menggunakan TF-IDF N-Gram dan Multinomial Naive Bayes M. Akbar Zidane; Yuli Praptomo Pamungkas Hari Sungkowo
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10141

Abstract

The number of users of the online game Apex Legends continues to increase along with the always active community, which also leads to an increase in the number of user reviews. In this condition, conducting manual review analysis becomes ineffective, especially due to the numerous reviews written in informal English, containing negation words, and also showing an imbalanced sentiment class distribution. In this study, the aim is to classify reviews from Apex Legends users on the Steam platform into positive and negative sentiments using the Multinomial Naive Bayes algorithm with TF-IDF weighting based on N-Gram features with a combination of Unigram and Bigram. The dataset was obtained through web scraping from the Steam platform with a total of 9,000 reviews, followed by preprocessing which resulted in 8,981 valid reviews. However, the data still showed class imbalance. The random undersampling process was then applied to obtain 5,512 balanced data points. The test results show that the model can achieve an accuracy of 0.8132 or 81.32%. For the negative class, the model obtained a precision of 0.79, recall of 0.85, and f1-score of 0.82, while the positive class obtained a precision of 0.83, recall of 0.78, and f1-score of 0.81. The trained model is also applied to a Streamlit based dashboard to support the visualization and prediction of new review sentiments. The contributions of this study are the application of combined N-Gram features (unigram and bigram) to Multinomial Naive Bayes for handling negation context and informal language, the use of random undersampling to address class imbalance, and the deployment of the trained model into a Streamlit-based dashboard that enables direct visualization and sentiment prediction of new reviews.
Prediksi Risiko Dropout Mahasiswa Menggunakan Decision Tree Dengan Pendekatan Feature Importance Roby Setiawan; Yuli Praptomo Pamungkas Hari Sungkowo
Jurnal QOSIM : Jurnal Pendidikan, Sosial & Humaniora Vol 4 No 3 (2026): 2026
Publisher : Yayasan pendidikan dzurriyatul Quran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61104/jq.v4i3.8952

Abstract

Tingginya angka putus kuliah (dropout) mahasiswa merupakan tantangan bagi institusi pendidikan tinggi karena berdampak pada kualitas perguruan tinggi dan masa depan akademik mahasiswa. Oleh karena itu, identifikasi dini terhadap mahasiswa yang berisiko penting untuk dilakukan. Penelitian ini menerapkan teknik data mining menggunakan algoritma Decision Tree untuk memprediksi potensi dropout mahasiswa berdasarkan rekam jejak akademik dan latar belakang demografis. Eksperimen dilakukan menggunakan dataset 'Predict Students' Dropout and Academic Success' dengan total 3.630 baris data valid dan 29 fitur prediktor. Proses pemodelan dilakukan dengan membagi dataset ke dalam proporsi 80% data latih dan 20% data uji. Hasil evaluasi menunjukkan bahwa model klasifikasi menghasilkan tingkat akurasi sebesar 88,57%, dengan nilai precision sebesar 88% dan recall sebesar 81% untuk kelas dropout. Analisis feature importance mengungkapkan bahwa fitur prediktor utama yang berkontribusi terhadap risiko dropout didominasi oleh tingkat kelulusan mata kuliah pada semester kedua, yang beriringan
Co-Authors Ahmad Naswin Deni Ambarwati Edi Sastrawijoyo Lukman Syafie M. Akbar Zidane M. Insan Nur Firdaus Moh Sidik Warsono Retno Widyarti Roby Setiawan Selfia Destiana Siti Rahayu