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All Journal Jurnal Pseudocode Journal of Information Systems Engineering and Business Intelligence Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Eksplora Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknoinfo Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Infomedia JURNAL PengaMAS MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Informatika dan Rekayasa Elektronik Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) Information System Journal (INFOS) JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer Jurnal Pengabdian Mitra Masyarakat (JPMM) International Journal of Advanced Science Computing and Engineering SmartComp Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen Scientific Journal of Informatics SWAGATI: Journal of Community Service Edu Komputika Journal Jurnal Pengabdian Masyarakat Inovasi Indonesia
Yoga Pristyanto
Universitas Amikom Yogyakarta
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Penerapan Metode Profile Matching pada Seleksi Ketua OSIS di SMA Negeri 2 Kasongan Kristianti, Fanny Novatriana; Pristyanto, Yoga; Rohman, Arif Nur
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 13, No 3: Desember 2024
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v13i3.2273

Abstract

This research builds a Decision Support System (DSS) for the Selection of Student Council Chair Candidates at SMA Negeri 2 Kasongan, replacing the conventional system which is subjective and less transparent. Conventional systems are often influenced by the committee's personal preferences, are time consuming, and lack accuracy because there are no specific criteria. The Profile Matching method in DSS provides an objective assessment through three aspects: file test, written test, and interview. The black box testing results show that the DSS functions well, the analysis results show the accuracy, precision, recall and specificity values are 0.8 (80%), which confirms the effectiveness of this method. These findings show that SPK is more accurate, objective and efficient than conventional systems.Keywords: Selection of Student Council Chair Candidates; Decision Support System; Profile Matching Method; Conventional System.  AbstrakPenelitian ini membangun Sistem Pendukung Keputusan (SPK) pada seleksi calon Ketua OSIS di SMA Negeri 2 Kasongan, menggantikan sistem konvensional yang subjektif dan kurang transparan. Sistem konvensional sering terpengaruh oleh preferensi pribadi panitia, memakan waktu, dan kurang akurat karena tidak ada kriteria khusus. Metode Profile Matching dalam SPK memberikan penilaian objektif melalui tiga aspek: tes berkas, tes tertulis, dan wawancara. Hasil pengujian blackbox menunjukkan bahwa SPK berfungsi dengan baik dan hasil analisis menunjukkan nilai akurasi, presisi, recall dan spesifisitas masing-masing sebesar 0.8 (80%), yang menegaskan efektivitas metode ini. Temuan ini menunjukkan bahwa SPK lebih akurat, objektif, dan efisien dibandingkan sistem konvensional. 
IMPLEMENTASI METODE PROFILE MATCHING DALAM PENERIMAAN SISWA BARU MTS DARUL MUTTAQIEN Anggi Thoat Ariyanto; Yoga Pristyanto; Arif Nur Rohman
Jurnal Informatika dan Rekayasa Elektronik Vol. 7 No. 2 (2024): JIRE NOPEMBER 2024
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/jire.v7i2.1296

Abstract

Penerimaan Peserta Didik Baru (PPDB) merupakan kegiatan rutin yang dilaksanakan di seluruh satuan pendidikan menjelang tahun pelajaran baru. Semakin baik kualitas sekolah, semakin banyak siswa yang tertarik mendaftarkan diri sebagai siswa baru. Berkaitan kualitas suatu sekolah tidak jauh kaitanya dengan proses penerimaan siswa baru. Sehingga penting untuk dilakukan seleksi terhadap calon siswa. Saat ini seleksi siswa di MTs Darul Muttaqien Kabupaten Merangin masih dilakukan secara manual sehingga dapat menimbulkan elemen subjektif, menghabiskan waktu yang lama kurang lebih 2 sampai 3 hari dan berpotensi menghasilkan data yang tidak akurat mengenai status kelulusan siswa. Mengacu pada masalah yang ada maka dirancang sebuah Sistem Pendukung Keputusan (SPK) dalam PPDB dengan menggunakan metode profile matching. Profile matching merupakan sebuah mekanisme atau  proses membandingkan antara kemampuan individu berdasarkan kriteria penilaian untuk mengetahui perbedaan nilai, juga dikenal sebagai (gap). Di samping itu, metode profile matching juga memperhitungkan konsistensi yang logis dalam penilaian untuk menetapkan prioritas yang lebih akurat dibandingkan dengan metode-metode lainya. Proses pengujian sistem dilakukan dengan menginputkan data yang sama ke dalam sistem yang telah dikembangkan. Sistem kemudian melakukan perhitungan, dan hasil pengujian menunjukkan bahwa perhitungan sistem memiliki akurasi 100%, sesuai dengan hasil perhitungan manual. Hal ini menunjukkan bahwa sistem yang dibangun mampu menghasilkan pemeringkatan yang konsisten dan sejalan dengan perhitungan manual.
SISTEM REKOMENDASI PARIWISATA GUNUNGKIDUL BERBASIS WEB MENGGUNAKAN METODE CONTENT-BASED FILTERING Sifa’ul Husna, Siti Okta; Nur Fajri , Ika; Pristyanto, Yoga
Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) Vol 6 No 3 (2024): JURSISTEKNI (Jurnal Sistem Informasi dan Teknologi Informasi)
Publisher : Universitas Nusa Putra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/jursistekni.v6i3.363

Abstract

Keindahan alam dan kekayaan budaya di Gunungkidul, Yogyakarta, menjadi daya tarik utama bagi wisatawan. Namun, dengan banyaknya pilihan tempat wisata, menentukan destinasi yang tepat bisa menjadi hal yang membingungkan. Untuk mengatasi hal ini, penelitian ini mengembangkan sistem rekomendasi pariwisata berbasis web dengan metode content-based filtering. Sistem ini diharapkan dapat membantu wisatawan dalam menemukan tempat wisata yang sesuai dengan minat dan preferensi mereka, sehingga meningkatkan pengalaman wisata di Gunungkidul. Penelitian ini berfokus pada pengembangan sistem rekomendasi yang memanfaatkan informasi deskripsi tempat wisata. Metode content-based filtering digunakan untuk menganalisis kesamaan antara deskripsi tempat wisata. Hasil analisis ini kemudian digunakan untuk merekomendasikan tempat wisata yang paling sesuai dengan minat dan preferensi wisatawan. Penelitian ini diharapkan dapat memberikan kontribusi dalam meningkatkan kualitas layanan wisata di Gunungkidul. Sistem rekomendasi pariwisata ini dapat menjadi alat yang bermanfaat bagi wisatawan dalam memilih destinasi wisata yang tepat, sehingga meningkatkan kepuasan dan loyalitas wisatawan terhadap Gunungkidul sebagai destinasi wisata
Evaluation of the Decision Tree Model for Air Condition Classification on the Global Air Pollution Dataset Sabella, Cindy Dinda; Pristyanto, Yoga
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8611

Abstract

Air pollution is an urgent global environmental problem, with significant impacts on public health and ecosystem stability. This research aims to develop an air quality classification model using the Global Air Pollution dataset from Kaggle, which consists of 23,463 rows of data and 12 features, including important variables such as Air Quality Index (AQI), PM2.5, NO2, and O3. Decision Tree, Random Forest, and Support Vector Machine (SVM) algorithms are applied to perform classification, with a focus on hyperparameter tuning to increase model accuracy. The research results show that the Decision Tree provides the best results with an accuracy of 99.89% after tuning hyperparameters using the Grid Search method. The SVM model showed an improvement of 94.89% to 99.32%, while Random Forest recorded an accuracy of 96.87% with no significant improvement after tuning. Importance feature analysis identified PM2.5 and AQI as the dominant factors in influencing air quality, with PM2.5 having the highest importance value of 0.93. This research confirms that machine learning can be an effective tool for integrating and classifying air pollution. It is hoped that the integration of this model into a real-time air quality monitoring system can help make more responsive and precise decisions in dealing with air pollution problems.
The Effect of Adaptive Synthetic and Information Gain on C4.5 and Naive Bayes in Imbalance Class Dataset Sulistiyono, Mulia; Wirasakti, Lucky Adhikrisna; Pristyanto, Yoga
International Journal of Advanced Science Computing and Engineering Vol. 4 No. 1 (2022)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (939.576 KB) | DOI: 10.62527/ijasce.4.1.70

Abstract

Class imbalance is a severe problem in classification due to the deep slope on the class axis. The dataset is dominated by the majority class, which has the potential for misclassification. Another problem in classification and clustering is that high-dimensional datasets are found that have the potential to affect the performance of classification algorithms in terms of computation and accuracy. In this study, the class imbalance was handled using the ADASYN k - NN resampling technique and the selection feature using Information Gain. Based on the evaluation results, the sampling contribution matrix can improve the classification model by improving the geometric mean value. The selection feature helps interpret data with more simple features but can reduce the accuracy of the results. The results showed that the implementation of ADASYN k-NN and Information Gain could increase the accuracy score and geometric mean score of Decision Tree C4.5 and Naive Bayes. For further work, this proposed method will be tested on multiclass imbalanced datasets.
Implementation of a Web-Based Waste Collection Data System Using QR Code Scanning Romadhon, Ibrahim Aji Fajar; Rohman, Arif Nur; Pristyanto, Yoga
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (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.v14i1.4782

Abstract

The suboptimal management of waste collection in urban areas significantly impacts environmental quality and public health. Yogyakarta City, which generates 644.69 tons of waste annually, can only manage 583.80 tons per year. Various initiatives have been implemented to improve waste management, yet challenges persist, such as limited temporary disposal sites, irregular waste collection schedules, and the absence of an effective and efficient system to assist waste collection officers in recording and tracking waste collection for each household.This study aims to develop a web-based Waste Collection Data System using QR Code Scanning, employing the waterfall method, which consists of the following stages: requirement analysis, design, development, testing, and maintenance. The system enables waste collection officers to log waste collection activities by scanning a QR code at each household and allows residents to access information regarding waste collection status, mandatory fees, collection schedules, and waste processing. The testing results demonstrate that all features function effectively as intended. The implementation of this system is expected to enhance the efficiency of waste collection data management, improve environmental quality, and increase community satisfaction in Yogyakarta City.
Pelatihan Pembuatan dan Pengelolaan Website untuk Meningkatkan Skill dan Wawasan IT pada Menwa IAIN Salatiga Windarni, Vikky Aprelia; Nugraha, Anggit Ferdita; Pristyanto, Yoga; Aziza, Rifda Faticha Alfa; Purwanto, Ibnu Hadi; Sunyoto, Andi
SWAGATI : Journal of Community Service Vol. 1 No. 2 (2023): July
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/swagati.2023v1i2.1037

Abstract

Perkembangan teknologi informasi yang semakin pesat membawa perubahan yang signifikan bagi personal maupun organisasi. Hal tersebut juga berlaku pada Organisasi Resimen Mahasiswa (Menwa) IAIN Salatiga, terutama untuk menyebarkan informasi kepada khalayak umum. Website menjadi salah satu teknologi yang dapat membantu menyebarkan dan berbagi informasi secara cepat dan mudah. Sayangnya, Sebagian besar anggota menwa IAIN bukan berasal dari bidang ilmu komputer sehingga awam terhadap proses pembuatan dan pengelolaan website. Disisi lain, adanya kemauan yang kuat untuk belajar perlu didukung melalui proses pelatihan dan pendampingan oleh tenaga ahli sehingga pengelolaan website nantinya dapat dilakukan oleh anggota Menwa secara mandiri. Selain itu, dengan adanya proses pelatihan serta pendampingan dalam pembuatan dan pengelolaan website diharapkan dapat memberikan manfaat bagi organisasi serta dapat meningkatkan skill dan wawasan IT bagi anggota organisasi tersebut
COMPARISON OF ENSEMBLE METHODS FOR DECISION TREE MODELS IN CLASSIFYING E. COLI BACTERIA Alvin Rahman Al Musyaffa; Yoga Pristyanto; Nia Mauliza
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.5972

Abstract

Certain strains of Escherichia coli (E. coli) can cause serious illness, so identifying dangerous strains with high accuracy is a priority in supporting public health and food safety. However, traditional machine learning methods, such as Decision Trees, are often not robust enough to handle the complexity of biological data. This research presents a solution by systematically evaluating seven ensemble methods, namely Adaboost, Gradient Boosting, XGBoost, LightGBM, Random Forest, Bagging, and Stacking, using a dataset that includes 336 E. coli samples with eight biological features. These models are evaluated based on accuracy, precision, recall, and F1 score, with parameter optimization to obtain the best results. The results show that XGBoost is superior with accuracy, recall, and F1 score of 88% and precision of 87%, outperforming other methods. This research has the advantage of a comprehensive approach in comparing various ensemble methods simultaneously, accompanied by the application of confusion matrix-based evaluation to ensure the accuracy of the results. Additionally, the ensemble approach proved to be more effective in handling complex data patterns and reducing bias in bacterial strain classification. These findings provide a significant contribution, namely a practical framework for improving laboratory diagnostics and public health surveillance, with machine learning-based solutions that are faster, more reliable, and applicable for both industrial and clinical environments. This research expands understanding of the potential of ensemble methods in microbiological data classification and provides new directions for modern diagnostic technology.
Perbandingan Algoritma Genetika dan Recursive Feature Elimination pada High Dimensional Data Pristyanto, Yoga; Wirantanu, Dipa
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5375

Abstract

The use of big data in companies is currently used in file processing. With large capacity files, it can affect the performance in terms of time in the company, so to overcome the problem of high-dimensional data, feature selection is used in selecting the number of features. On the WDC dataset with 30 features and 569 data points, feature selection is performed using the Recusive Feature Elimination (RFE) and Genetic Algorithm (GA) models. Then, a comparison of evaluation values is made to determine which feature selection is best for solving the problem. From the 14 tables of evaluation results and discussion in tables 1 to 14, it is found that in the evaluation of accuracy and the use of weighted macros on precision, recall, and f1 score, using GA selection features has slightly higher results than RFE, so it is concluded that GA selection features are better at solving problems in high-dimensional data.
The Effect of Resampling Techniques on Model Performance Classification of Maternal Health Risks Nia Mauliza; Aisha Shakila Iedwan; Yoga Pristyanto; Anggit Dwi Hartanto; Arif Nur Rohman
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5934

Abstract

Indonesia's maternal mortality rate was the second highest in ASEAN, reflecting the problem of class imbalance in maternal health data. This research aimed to improve prediction accuracy in the classification of pregnant women's diseases through the application of various resampling methods. The methods used in this research included Synthetic Minority Over-sampling Technique (SMOTE), SMOTE-Edited Nearest Neighbor (SMOTE-ENN), Adaptive Synthetic Sampling (ADASYN), and ADASYN-ENN, using five classification algorithms: Decision Tree, K-Nearest Neighbor (KNN), Naïve Bayes, Random Forest, and Support Vector Machine (SVM). Performance evaluation was carried out using accuracy, precision, recall, and F1-score metrics to determine the best method and algorithm. The results showed that the SMOTE-ENN and ADASYN-ENN methods significantly improved the model's performance in predicting maternal disease. Random Forest and Decision Tree algorithms showed the best results in terms of accuracy and consistency. These findings provided practical guidance for the application of resampling techniques in the classification of pregnant women's health data, which could contribute to improving the quality of maternal health services in Indonesia.
Co-Authors Acihmah Sidauruk Aditya Yoga Pratama Afrig Aminuddin Aisha Shakila Iedwan Akhmad Dahlan Alvin Rahman Al Musyaffa Andi Sunyoto Anggi Thoat Ariyanto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto, Anggit Dwi Anggita, Sharazita Dyah Anna Baita Arif Nur Rohman arif nur rohman Asti Astuti, Ika Atik Nurmasani ATIK NURMASANI Atik Nurmasani Barus, Herianta Bety Wulan Sari Bety Wulan Sari, Bety Wulan Bligania Bligania Cherfly Kaope Donni Prabowo, Donni Dwi Hartanto, Anggit Dyah Anggita, Sharazita Eli Pujastuti, Eli Eza Nanda Fadhilah Dwi Ananda Fajri, Ika Nur Fauzy, Marwan Noor Gagah Gumelar Gita Cahyani Hendra Kurniawan Heri Sismoro Hidayat, Kardilah Rohmat Ibnu Hadi Purwanto Ibrahim Aji Fajar Romadhon Iedwan, Aisha Shakila Ike Verawati Ikmah Ikmah Irfan Pratama Istikomah Khoiruddin, Lukman Kono, Maria Fatima Kristianti, Fanny Novatriana Lucky Adhikrisna Wirasakti Mambaul Hisam Marcheilla Trecya Anindita Maulana, Ariefhan Mauliza, Nia Mukarabiman, Zulfikar Mulia Sulistiyono Nia Mauliza Nia Mauliza Nugraha, Anggit Ferdita Nuri Cahyono Nurindah A Amari Purwati, Sintia Eka Putra, Frahma Aditya Rahman Saputra, Rahman Rifda Faticha Alfa Aziza Rizky Hafizh Jatmiko Rohmad Fajarudin Rohman, Arif Nur Romadhon, Ibrahim Aji Fajar Rospita, Andri Sabella, Cindy Dinda Sifa’ul Husna, Siti Okta Sumarni Adi Windarni, Vikky Aprelia Wirantanu, Dipa Wirasakti, Lucky Adhikrisna Wiwi Widayani Wulandari, Irma Rofni Yanuar Nur Kholik Yudiyanto, Muhammad Resa Arif Yuli Astuti Zein, Aditya Ahmad