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All Journal CESS (Journal of Computer Engineering, System and Science) Sinkron : Jurnal dan Penelitian Teknik Informatika Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Jurnal Teknik Komputer AMIK BSI Paradigma Jurnal Pilar Nusa Mandiri Indonesian Journal of Artificial Intelligence and Data Mining JITK (Jurnal Ilmu Pengetahuan dan Komputer) JISICOM (Journal of Information System, Infomatics and Computing) Jurnal Mantik Jurnal Informatika dan Rekayasa Elektronik Jurnal Infortech Jurnal Teknik Informatika (JUTIF) Jurnal Sistem Informasi Jurnal AbdiMas Nusa Mandiri Indonesian Journal of Networking and Security - IJNS Teknologika Journal of Applied Agricultural Science and Technology Paradigma Jurnal Ticom: Technology of Information and Communication
Verry Riyanto
Universitas Bina Sarana Informatika
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Other

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Penerapan Algoritma K-means Clustering Pada Pola Kunjungan Perpustakaan menggunakan Soft system methodology Nafila, Dzhikrokhatun; Verry Riyanto
Jurnal Ticom: Technology of Information and Communication Vol 13 No 1 (2024): Jurnal Ticom-September 2024
Publisher : Asosiasi Pendidikan Tinggi Informatika dan Komputer Provinsi DKI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70309/ticom.v13i1.126

Abstract

Ruang Publik Terpadu Ramah Anak (RPTRA) Manunggal Juang Sukapura, yang diresmikan pada 21 April 2016, menyediakan berbagai fasilitas termasuk perpustakaan yang aktif dikunjungi oleh berbagai kelompok usia, mulai dari balita hingga lansia. Keberagaman ini menimbulkan tantangan bagi pengelola perpustakaan dalam memahami pola perilaku dan preferensi bacaan pengunjung. Penelitian ini menggunakan metode clustering K-means untuk menganalisis 119 data kunjungan dari Januari sampai April 2024, dengan pendekatan Soft system methodology (SSM) untuk memahami kompleksitas masalah pengelolaan perpustakaan. Penelitian ini bertujuan untuk mengidentifikasi pola kunjungan berdasarkan kelompok usia, menentukan kelompok pengunjung dominan, serta merancang strategi untuk meningkatkan pelayanan dan menyusun koleksi buku yang lebih sesuai dengan preferensi setiap kelompok usia. Hasil klasterisasi menunjukkan terbentuknya tiga klaster utama: C0 (70 data), C1 (32 data), dan C2 (17 data), yang masing-masing mencerminkan karakteristik kelompok usia tertentu. Klaster 0 menunjukkan proporsi pengunjung yang merata dari berbagai kelompok usia, Klaster 1 lebih didominasi oleh anak-anak dan remaja, sementara Klaster 2 memiliki jumlah pengunjung lansia yang signifikan. Hasil klasterisasi ini memberikan pemahaman yang mendalam tentang preferensi dan kebutuhan setiap kelompok usia dalam kunjungan ke perpustakaan
Deep Learning Approaches for Plant Disease Diagnosis Systems: A Review and Future Research Agendas Riyanto, Verry; Nurdiati, Sri; Marimin, Marimin; Syukur, Muhamad; Neyman, Shelvie Nidya
Journal of Applied Agricultural Science and Technology Vol. 9 No. 2 (2025): Journal of Applied Agricultural Science and Technology
Publisher : Green Engineering Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55043/jaast.v9i2.308

Abstract

To identify novel advancements in plant diseases detection and classification systems employing Machine Learning (ML), Deep Learning (DL), and Transfer Learning (TL), this research compiled 111 peer-reviewed papers published between 2019 and early 2023. The literature was sourced from databases such as Scopus and Web of Science using keywords related to deep learning and leaf disease. A structured analysis of various plant disease classification models is presented through tables and graphics. This paper systematically reviews the model approaches employed, datasets utilized, countries involved, and the validation and evaluation methods applied in plant disease identification. Each algorithm is annotated with suitable processing techniques, such as image segmentation and feature extraction, along with standard experimental metrics, including the total number of training/testing datasets utilized, the quantity of disease images considered, and the classifier type employed. The findings of this study serve as a valuable resource for researchers seeking to identify specific plant diseases through a literature-based approach. Additionally, the implementation of mobile-based applications using the DL approach is expected to enhance agricultural productivity.
MENGOPTIMALKAN PREDIKSI GAGAL JANTUNG DENGAN KOMBINASI SVM DAN FORWARD SELECTION Riyanto, Verry; Destiana, Henny; Prihatin, Titin; Sugiono; Wijaya, Ganda
Jurnal Informatika dan Rekayasa Elektronik Vol. 8 No. 1 (2025): JIRE APRIL 2025
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/jire.v8i1.1541

Abstract

Gagal jantung merupakan salah satu kondisi kesehatan kritis dengan angka kematian yang terus meningkat, dengan permasalahan yang ada diagnosis tradisional seringkali kurang akurat dan efisien sehingga diperlukan metode diagnosis dini yang lebih presisi dan efisien. Penelitian sebelumnya telah meningkatkan akurasi prediksi dengan berbagai metode namun masih terbatas dalam pemilihan fitur optimal dan efisiensi pemodelan. Oleh karena itu, penelitian ini bertujuan untuk menganalisis kinerja kernel pada algoritma Support Vector Machine (SVM) seperti Dot, Radial, Polynomial dan menganalisis efektivitas Forward Selection (FS) dalam memilih fitur paling signifikan guna mengoptimalkan prediksi risiko gagal jantung. Hasil penelitian menunjukkan bahwa kernel Radial dengan FS memiliki performa terbaik dengan AUC 0.881, Accuracy 84,64%, dan Recall 92,55%. Fitur time dan serum_creatinine terbukti paling signifikan dalam meningkatkan performa model. Penelitian ini membuktikan bahwa kombinasi antara SVM dan FS mampu menghasilkan solusi yang lebih presisi dan efisien dalam diagnosis dini gagal jantung dibandingkan pendekatan sebelumnya. Hasil ini diharapkan dapat mendukung pengembangan sistem prediksi berbasis kecerdasan buatan untuk aplikasi klinis yang lebih andal.
USABILITY ENGINEERING ANALYSIS ON MY BEST E-LEARNING APPLICATION UNIVERSITY OF BINA SARANA INFORMATIKA Hartati, Tri; Hikmah, Noer; Riyanto, Verry
Journal of Information System, Informatics and Computing Vol 7 No 2 (2023): JISICOM (December 2023)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisicom.v7i2.1279

Abstract

After the Covid-19 pandemic ended, the lecture system at several universities in Indonesia began to gradually improve. The lecture system for several courses is carried out face-to-face. However, there are still several courses that are conducted online using e-learning applications with various considerations for the needs of the university. Bina Sarana Informatics University (UBSI) is a technology-based university where the learning system has implemented internet technology even long before the Covid-19 pandemic occurred. UBSI always strives to continue to improve the quality of the e-learning system that has been implemented. This can be seen from the development of e-learning applications for the lecture process which makes it easier for lecturers and students to access and absorb information related to campus academics. My Best is an e-learning application that is currently used in the KBM process in the UBSI environment and to improve the quality of the application used it is necessary to carry out a usability engineering evaluation. This analysis method consists of several stages, namely platform constraints, general design principles, conceptual model and screen design. The results of the usability analysis on the My Best e-learning application at Bina Sarana Informatika University have complete stage coverage, this means that My Best is an e-learning application in the very good category.
DECISION TREE OPTIMIZATION IN HEART FAILURE DIAGNOSTICS: A PARTICLE SWARM OPTIMIZATION APPROACH Sumarna, Sumarna; Sartini, Sartini; Pangesti, Witriana Endah; Suryadithia, Rachmat; Riyanto, Verry
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.1815

Abstract

The rapid advancement of technology has made the implementation of accurate diagnostic methods for serious diseases like heart failure extremely important. Heart failure, being a leading cause of death worldwide, necessitates precise and accurate diagnostic techniques. The problem with conventional diagnostic methods is that they often fail to effectively accommodate the complexity of clinical data, leading to an increase in mortality rates due to heart failure. Previous research has employed various data analysis methods, but there are still fluctuations in the accuracy of results. The aim of this study is to enhance the accuracy of heart failure diagnosis by integrating the Decision Tree (DT) method with Particle Swarm Optimization (PSO) optimization. This research involves collecting and preprocessing heart failure data, followed by the development of a DT model. This model is then optimized using the PSO technique. The study uses a dataset from the UCI Repository, involving testing and validation processes to measure the model's effectiveness. The results show a significant improvement in accuracy and the Area Under Curve (AUC) after applying PSO. Accuracy increased from 79.92% to 85.29%, and AUC from 0.706% to 0.794%. The conclusion is that the integration of DT and PSO successfully improved the accuracy and reliability of the model in diagnosing heart failure. This innovation offers potential for further research in integrating optimization techniques in health data analysis, with the possibility of application in various clinical scenarios.
Co-Authors Abdul Hamid Abdul Hamid Achmad Maezar Bayu Aji Biktra Rudianto - STMIK Nusa Mandiri Jakarta Dahlia Dahlia Eni Pudjiarti Eri Riana Ganda Wijaya Ganda Wijaya Ganda Wijaya Ganda Wijaya, Ganda Hafis Nurdin Henny Destiana Herlambang Brawijaya Hikmah, Noer Imam Nawawi Jajang Jaya Purnama Marimin Marimin Mohammad Roffi Suhendry Muhamad Syukur Nafila, Dzhikrokhatun Niar Adisty Putra Nuraeni Herlinawati Rahmatul Husna Ridwansyah Ridwansyah Rudianto, Biktra Sartini Sartini Shelvie Nidya Neyman Shena Arrahima Qolby Siti Faizah Sri Nurdiati Sri Rahayu Sugiono, Sugiono Sumarna Sumarna . Sumarna Sumarna Sumarna Sumarna Suryadithia, Rachmat Susy Rosyida Toto Haryanto Tri Hartati, Tri WITRIANA ENDAH PANGESTI Yana Iqbal Maulana