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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 Teknologi Informasi dan Terapan (J-TIT) INTI Nusa Mandiri 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.
AI-DRIVEN ACADEMIC SCREENING: PENGEMBANGAN SISTEM REVIEWER OTOMATIS BERBASIS AI AGENT Riyanto, Verry; Saryoko, Andi; Anton; Mazia, Lia; Nurmalasari; Mardiana, Tati
INTI Nusa Mandiri Vol. 20 No. 2 (2026): INTI Periode Februari 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v20i1.6793

Abstract

An artificial intelligence (AI)-based research proposal submission system is an innovative solution to improve efficiency and transparency in the academic selection process. This study develops a web-based system using the Laravel framework integrated with AI Agent to automatically review the title and abstract of lecturers' research proposals. This system is designed with a hybrid training approach, combining Supervised Learning (labeled data) and Reinforcement Learning from Human Feedback (RLHF), and utilizing Natural Language Processing (NLP) techniques for semantic analysis. The implementation results show that the system is able to evaluate research proposals with high accuracy, including checking title-abstract alignment, identifying problem backgrounds, and assessing originality. The system also provides real-time statistics and evaluation records, supporting more objective decision making. The contribution of the research lies in the use of AI to automate academic processes, reduce the workload of human reviewers, and improve the integrity of the research roadmap
Grouping Data in Predicting Infant Mortality Using K-Means and Decision Tree Ridwansyah, Ridwansyah; Riyanto, Verry; Hamid, Abdul; Rahayu, Sri; Purnama, Jajang Jaya
Paradigma - Jurnal Komputer dan Informatika Vol. 24 No. 2 (2022): September 2022 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/paradigma.v24i2.1399

Abstract

Death is something that we cannot avoid where, when and how death comes. The high infant mortality rate is the main thing and the Indonesian government must prioritize, one of the government's efforts to reduce infant mortality is by conducting a surveillance program, namely PWS KIA where the program is uniting the health of mothers and babies in the local area, basically there are several infant deaths that have causes from the time of pregnancy, accidents, disasters, diseases or because it is destiny from God, for that research is carried out in classifying infant mortality data. For grouping infant mortality data, a K-Means method is needed to analyze data by carrying out a data modeling process without supervision or also known as unsupervised learning. In showing the centroid in the early stages of the k-means algorithm, it is very influential on the results of the cluster carried out on the infant mortality dataset. taken from data.go.id with different centroid results. The results of the clustering model pattern that can be trusted by the government or the Health department to prevent infant mortality. From the clustering results, four labels are tested again using the decision tree algorithm.
Optimization of the YOLOv7 Object Detection Algorithm for Estimating the Amount of Apple Harvest Riyanto, Verry; Nawawi, Imam; Ridwansyah, Ridwansyah; Wijaya, Ganda; Haryanto, Toto
Paradigma - Jurnal Komputer dan Informatika Vol. 25 No. 1 (2023): March 2023 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v25i1.1809

Abstract

The increasing population consumed in high production and food needs for survival. Apples are one of the crop harvest products in Indonesia whose needs are increasing, because they are not only needed for human vitamins but can be used as hand fruit or a form of gratitude to those who receive the fruit. In the process of harvesting apples in agricultural land, harvesting is often found which is not feasible in the hands of consumers because it takes too long for apples to not be harvested when the condition of the fruit is feasible in maturity. Therefore, the authors approach this problem by processing the image results obtained to form a detection model, whether the apples are said to be feasible to be harvested immediately and from the image results it can also be calculated the number of fruits captured by the image model , feature enhancements Estimates on objects from this image model are expected to provide more timely harvest predictions in order to provide longer aging of apples and good fruit quality after reaching consumers
Digital Image-Based Chili Quality Detection Using a Web-Based Convolutional Neural Network Purnama, Jajang Jaya; Rahayu, Sri; Ridwansyah, Ridwansyah; Riyanto, Verry
Jurnal Teknologi Informasi dan Terapan Vol 12 No 2 (2025): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i2.453

Abstract

ABSTRACT Chili is one of the main horticultural commodities in Indonesia, with high economic value and stable market demand. Accurate determination of chili quality levels is an important factor in maintaining quality, selling price, and distribution efficiency. Until now, the process of assessing chili quality has generally been carried out manually through direct visual inspection by experts or field officers. This traditional approach has limitations, such as varying levels of accuracy due to assessor subjectivity and the limited availability of experts. Advancements in digital image processing technology, particularly deep learning, offer opportunities to develop more accurate and consistent automated detection systems. This study proposes a Convolutional Neural Network (CNN) model to classify chili quality levels based on digital images, which is then integrated into a web-based application. This study uses a dataset of 405 chili images from 11 varietal categories, each labeled with quality (good, pest-infested, or unknown), which undergoes preprocessing stages including resizing, normalization, and data augmentation. The CNN model was designed with convolutional layers, max-pooling, dense layers, and a Softmax activation function, and was trained using the Adam optimizer and Categorical Cross-Entropy Loss. The web application implementation was carried out using the Flask framework, allowing users to upload images and obtain prediction results in real time. The testing results showed that the developed CNN model achieved an accuracy of 1.000 on the test data, with reliable detection performance under variations in lighting and image backgrounds. This research contributes to the development of smart agriculture technology by providing an accurate, fast, and easily accessible solution for chili quality detection
Co-Authors Abdul Hamid Abdul Hamid Abdul Hamid Achmad Maezar Bayu Aji Andi Saryoko Anton 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 Imam Nawawi, Imam Jajang Jaya Purnama Lia Mazia, Lia Marimin Marimin Mohammad Roffi Suhendry Muhamad Syukur Nafila, Dzhikrokhatun Niar Adisty Putra Nuraeni Herlinawati Nurmalasari Rahmatul Husna Ridwansyah Ridwansyah Ridwansyah Ridwansyah Rudianto, Biktra Sartini Sartini Shelvie Nidya Neyman Shena Arrahima Qolby Siswahyudianto Siti Faizah Sri Nurdiati Sri Rahayu SRI RAHAYU Sumarna Sumarna . Sumarna Sumarna Sumarna Sumarna Suryadithia, Rachmat Susy Rosyida Tati Mardiana, Tati Toto Haryanto Tri Hartati, Tri WITRIANA ENDAH PANGESTI Yana Iqbal Maulana