Article Per Year (5 Year)
p-Index From 2021 - 2026
7.049
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Pendidikan Teknologi dan Kejuruan Yustisia Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal EECCIS Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Jurnal Komputasi Jurnal Sains dan Informatika Jurnal Teknoinfo ILKOM Jurnal Ilmiah Jurnal Ilmiah Media Sisfo Jurnal Tekno Kompak JUTIS : Jurnal Teknik Informatika EKONOMI BISNIS Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) Jurnal Abadimas Gorontalo JTIKOM: Jurnal Teknik dan Sistem Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Ilmiah Infrastruktur Teknologi Informasi Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service Jurnal Pendidikan dan Teknologi Indonesia KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Telematics and Information Technology (TELEFORTECH) Journal of Engineering and Information Technology for Community Service Jurnal Media Borneo Jurnal Informatika Polinema (JIP) Jurnal Kecerdasan Buatan dan Teknologi Informasi Jurnal Rekayasa Perangkat Lunak Green Engineering: Journal of Engineering and Applied Science JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Mitra Jurnal Pengabdian Masyarakat Multidisiplin (MJPMM) Global Science: Journal of Information Technology and Computer Science Jurnal Komputasi Jurnal Elektronika dan Telekomunikasi
Agus Wantoro
Universitas Aisyah Pringsewu
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Other

Published : 68 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 68 Documents
Search

 3   4   5   6   7 
Implementasi Metode Naive Bayes pada Sistem Pakar Diagnosis Penyakit Kutu Ikan Gurami (Argunus Indicus) Agus Wantoro; Heni Sulistiyani; Yodhi Yuniarthe; Arie Setya Putra; Apri Candra Widyawati; Nanda Putra Wicaksono
Jurnal Komputasi Vol. 10 No. 1 (2022)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v10i1.2956

Abstract

Ikan Gurami (Oshpronemus gouramy) adalah salah satu ikan ekonomis air tawar penting di subsektor perikanan budi daya atau akuakultur (aquaculture), khususnya budi daya air tawar (freshwater aquaqulture).  Penyakit pada ikan gurami selain dapat menimbulkan kerugian berupa kematian ikan juga dapat menurunkan kualitas ikan yaitu kesegaran, warna, dan cacat tubuh yang kesemuanya tentu saja akan berpengaruh pada harga jual/nilai ekonomis ikan tersebut. Adapun kematian yang ditimbulkannya dapat mencapai 50%-100%. Untuk mengurangi kerugian akibat tingkat kematian yang tinggi maka dibutuhkan seorang pakar dalam melakukan diagnosis penyakit ikan. Faktanya tidak semua peternak ikan gurami memahami cara melakukan diagnosis, oleh karena itu dibutuhkan sebuah sistem pakar yang dapat digunakan untuk membantu peternak untuk diagnosis penyakit kutu ikan berdasarkan gejala. Hasil evaluasi sistem menggunakan 20 (dua puluh) data gejala ikan yang diperoleh dari peternak ikan gurami tahun 2021 yang dibandingkan dengan keyakinan pakar lalu dihitung menggunakan tabelconfusion matrix didapatkan nilai accuracy sebesar 94.2%, precision 95%, sensiivity 95% dan specivity 93.3%. Hasil evaluasi membuktikan bahwa metode Naïve Bayes berhasil memberikan hasil diagnosis yang baik, sehingga sistem yang dikembangkan dapat digunakan untuk oleh peternak ikan dalam melakukan diagnosis pada penyakit ikan gurami
KLASTERISASI DATA PENJUALAN BERDASARKAN WILAYAH MENGGUNAKAN METODE K-MEANS PADA PT XYZ Elin Mayoana Fitri; Ryan Randy Suryono; Agus Wantoro
Jurnal Komputasi Vol. 11 No. 2 (2023)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v11i2.12582

Abstract

PT XYZ merupakan perusahaan yang bergerak dalam bidang distributor minuman ringan yang ada di Lampung. Permasalahan yang dihadapi perusahaan saat ini dalam mengelola data transaksi penjualan di setiap cabangnya masih dilakukan pengelompokkan secara manual sehingga menjadi kurang efisien. Penelitian ini bertujuan untuk memberikan kemudahan bagi perusahaan dalam mengelola data transaksi penjualannya untuk mengetahui tingkat penjualan produk pada setiap cabangnya. Penelitian dilakukan dengan menggunakan metode clustering algoritma k-means, dan menggunakan bahasa pemrograman python. Data akan di clustering ke dalam 3 cluster yaitu penjualan tertinggi, penjualan, sedang, dan penjualan terendah. Hasil dari penelitian menunjukkan bahwa penjualan dengan nilai tertinggi adalah pada region 3, penjualan dengan nilai sedang berada pada region 11 dan penjualan dengan nilai terendah yaitu pada region 4. Pengujian terhadap hasil clustering dalam penelitian ini menggunakan metode silhouette score untuk mengetahui jumlah klaster yang optimal. Hasilnya didapatkan skor untuk klaster data penjualan berdasarkan region adalah 0,78 dan untuk klaster data penjualan berdasarkan outlet adalah 0,58. Skor tersebut menunjukkan bahwa jumlah klaster yang dihasilkan masuk kedalam kategori baik karena tidak mendekati -1. Berdasarkan hasil klasterisasi tersebut diharapkan dapat menjadi rekomendasi PT XYZ dalam menentukan strategi penjualan sebagai upaya meningkatkan keuntungan bagi perusahaan.
Analysis of recommendations for recipients of COVID-19 cash social assistance financing the ministry of social affairs Susanto, Erliyan Redy; Rusliyawati, Rusliyawati; Wantoro, Agus; Purnama, Citra Andini; Diasari, Itce
ILKOM Jurnal Ilmiah Vol 14, No 2 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i2.1138.126-133

Abstract

In order to solve the problems that exist in the economic aspect due to the COVID-19 pandemic in Indonesia, the government has implemented various programs related to economic recovery. One of these programs is cash social assistance (BST). During the implementation of the social assistance program in various regions, it was reported that the recipients of the program were not properly targeted. Based on the results of a survey from one of the leading universities in Indonesia, it is known that many social assistance programs related to the impact of the COVID-19 pandemic are suspected to have not been in accordance with their designation. Based on this, the research was conducted in Bandar Lampung City. The purpose of this study is to conduct an analysis for recommendations for prospective BST recipients, namely people affected by Covid-19. The method used is profile matching by taking samples in the Jagabaya village, Bandar Lampung City. The criteria used include the work of the head of the family, wife's work, home status, number of dependents and ID cards. Based on the results of an interview with one of the BST officials in Bandar Lampung City, in this study the criteria were grouped into core factors and secondary factors. The results of the research can be used by stakeholders as recommendations for prospective BST recipients in Bandar Lampung City. Based on the results of an interview with one of the BST officials in Bandar Lampung City, in this study the criteria were grouped into core factors and secondary factors. The results of the research can be used by stakeholders as recommendations for prospective BST recipients in Bandar Lampung City. Based on the results of an interview with one of the BST officials in Bandar Lampung City, in this study the criteria were grouped into core factors and secondary factors. The results of the research can be used by stakeholders as recommendations for prospective BST recipients in Bandar Lampung City.
Trust Centric Machine Learning Framework for Secure Decision Making in Decentralized Digital Service Ecosystems Deny Prasetyo; Siska Narulita; Ahmad Jurnaidi Wahidin; Rosalina Yani Widiastuti; Suyahman Suyahman; Very Dwi Setiawan; Agus Wantoro
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 4 (2025): December: Global Science: Journal of Information Technology and Computer Scienc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i4.197

Abstract

This study introduces a trust centric machine learning framework designed to improve decision making reliability and security in decentralized digital service ecosystems. Traditional machine learning models often focus on accuracy and efficiency but fail to address the challenges of trust and security in decentralized environments. In contrast, the proposed framework integrates dynamic trust indicators and employs Federated Learning (FL) to ensure privacy while enhancing decision making performance. The framework also incorporates Zero Knowledge Proofp based Verifiable Machine Learning (ZKP-VML), which ensures transparency and security without compromising sensitive data. Through continuous real time trust assessments, the framework adapts to changing conditions, improving the accuracy and reliability of decisions in environments where participants may not fully trust each other. The application of this framework in autonomous vehicles and IoT networks demonstrated its ability to make robust, secure decisions, even in complex and uncertain scenarios. The framework’s ability to incorporate both trust and security into its decision making processes sets it apart from traditional models, which typically do not address the trustworthiness of data or participants. This research highlights the importance of integrating trust and security into machine learning models, particularly in decentralized systems, and offers a robust solution to trust management challenges. However, challenges such as scalability and computational efficiency remain, and future work should focus on enhancing these aspects, along with exploring the framework's applicability in other decentralized domains like finance or supply chain management. The integration of privacy preserving technologies and improvements in adversarial robustness are also potential areas for future research.
Sustainable Precision Agriculture Irrigation System Using Edge Computing and Renewable Energy Integration for Water Conservation and Climate Adaptation Agus Wantoro; Ferly Ardhy; Fahlul Rizki; Ahmad Budi Trisnawan; Yulaikha Mar’atullatifah; Rachmat Setiabudi
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 2 (2025): April : Green Engineering: International Journal of Engineering and Applied Sci
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i2.288

Abstract

The integration of solar powered IoT irrigation systems in precision agriculture offers a sustainable solution to address water scarcity and enhance crop productivity. By leveraging real time data from soil sensors, weather APIs, and machine learning algorithms, these systems optimize irrigation schedules and improve water use efficiency. This research explores the potential of integrating renewable energy sources, such as solar power, with edge computing in smart irrigation systems to promote sustainable agricultural practices. The study aims to evaluate the performance of the proposed system in terms of water savings, crop yield, energy efficiency, and adaptability to varying climate conditions. Literature Review: Previous studies highlight the importance of smart irrigation systems in reducing water waste and improving crop yield through real time monitoring and automated decision making. However, existing systems often lack the integration of renewable energy and edge computing, which are critical for ensuring sustainability and operational efficiency in rural agricultural settings. The combination of renewable energy with IoT devices offers a promising solution to reduce energy costs and carbon emissions, while edge computing enhances real time data processing, ensuring prompt and accurate irrigation adjustments. Materials and Method: The proposed system integrates solar powered IoT devices, soil moisture sensors, weather data APIs, and edge computing devices to manage irrigation. Machine learning algorithms and evapotranspiration models are used to predict irrigation needs and optimize scheduling based on real time data. The system's performance is evaluated through metrics such as water savings percentage, crop yield improvements, and energy consumption, with a comparative analysis against traditional irrigation methods. Results and Discussion: The results indicate that the system successfully reduces water usage by 30% to 40%, increases crop yield by 25%, and operates with energy autonomy, powered entirely by solar energy. The system's adaptability to varying climate conditions ensures optimal crop growth, even under environmental stresses. The integration of renewable energy and edge computing significantly enhances the sustainability and efficiency of irrigation systems.
EVALUATION OF IMBALANCE CLASS HANDLING STRATEGIES ON MACHINE LEARNING MODEL PERFORMANCE Verdian, Arry; Wantoro, Agus
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.459

Abstract

Breast Cancer Dataset (BCD) represents a critical health problem due to the increasing prevalence of breast cancer and the importance of early detection of recurrence. Machine Learning (ML) approaches have been widely applied to support diagnosis and prediction; however, class imbalance remains a major challenge, where the majority class (“no-recurrence-events”) significantly outnumbers the minority class (“recurrence-events”). This imbalance can lead to biased models that fail to accurately detect recurrence cases. This study aims to evaluate the effectiveness of class imbalance handling using the Synthetic Minority Over-sampling Technique (SMOTE) on several ML models, including Decision Tree, Naïve Bayes, k-Nearest Neighbors (k-NN), and Random Forest. The dataset used consists of 286 records with 9 features obtained from the UCI Machine Learning repository. Data preprocessing was performed, including handling missing values and outliers, followed by class balancing using SMOTE. Model evaluation was conducted using 10-fold cross-validation and performance metrics such as accuracy, precision, recall, and F1-score. The results show that the application of SMOTE significantly improves model performance, with an average accuracy increase of 11.85%. Among the evaluated models, Random Forest combined with SMOTE achieved the best performance, with an accuracy of 79.79%. In contrast, models such as Naïve Bayes and k-NN demonstrated relatively lower performance. Overall, this study confirms that handling class imbalance using SMOTE can enhance classification performance, particularly in improving the detection of minority classes in breast cancer recurrence prediction tasks.
Hybrid Fuzzy Logic dan Profile Matching untuk Meningkatkan Klasifikasi Obat Hipertensi Wantoro, Agus; Ariwibowo, Catur; Rahmandini, Hafizhah Harjiati
Jurnal Teknik Informatika dan Sistem Informasi Vol 12 No 1 (2026): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v12i1.13480

Abstract

Classification of hypertension drugs has been carried out using various methods, but the combination of Fuzzy Logic and Profile Matching (F-PM) for hypertension drug classification has not been widely reported. This study develops a new proposal with a different approach, namely combining Fuzzy Logic with the Profile Matching method. This method was evaluated using fifty clinical datasets taken from www.kaggle.com. Experimental results show that the application of Fuzzy Logic to the Profile Matching method can increase accuracy by 20.18% or 98.39%. This study also compares it with other classification methods. The results of the performance comparison show that the proposed approach is superior. This approach can be a reference for many future studies.
Feature Selection and Class Imbalance Machine Learning for Early Detection of Thyroid Cancer Recurrence: A Performance-Based Analysis Agus Wantoro; Wahyu Caesarendra; Admi Syarif; Hari Soetanto
Jurnal Elektronika dan Telekomunikasi Vol. 25 No. 2 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.758

Abstract

Early detection of thyroid cancer recurrence is a crucial factor in patient survival and treatment effectiveness. Misdetection results in disease severity, high cost, recovery time, and decreased service quality. In addition, the main challenges in developing a Machine Learning (ML)-based detection decision support system are class imbalance in medical data and high feature dimensions that can affect model accuracy and efficiency. This study proposes a feature selection-based approach and class imbalance handling to improve the performance of early detection of Thyroid cancer. Several feature selection techniques, such as Information Gain (IG), Gain Ratio (GR), Gini Decrease (GD), and Chi-Square (CS), can select features based on weighted ranking. In addition, to overcome the imbalanced class distribution, we use the Synthetic Minority Over-Sampling Technique (SMOTE). ML classification models such as k-NN, Tree, SVM, Naive Bayes, AdaBoost, Neural Network (NN), and Logistic Regression (LR) are tested and evaluated based on a confusion matrix, including accuracy, precision, recall, time, and log loss. Experimental results show that the combination of imbalanced class handling strategies significantly improves the prediction performance of ML algorithms. In addition, we found that the combination of CS+NN feature selection techniques consistently showed optimal performance. This study emphasizes the importance of data pre-processing and proper algorithm selection in the development of a machine learning-based thyroid cancer detection system.
Co-Authors ., Rusliyawati Adam Japal Ade Surahman Adi Sucipto, Adi Adit Nurmansyah Admi Syarif Agum Anantama Ahmad Jurnaidi Wahidin Andini, Dwi Yana Ayu Apri Candra Widyawati Apri Candra Widyawati Ari Sulistiawati Ari Sulistyawati Aria Dadi Wibisono Arie Setya Putra Ariwibowo, Catur Arry Verdian Arry Verdian Aryani, Venty Aviv Fitria Yulia Ayu Andini, Dwi Yana Ayu Sangging, Putu Ristyaning Bintoro, Panji Damayanti Daniel Prasetyo Tarigan Dedi Darwis Deny Prasetyo Devi Utari Diasari, Itce Dikpride Despa Dikpride Despa Dimas Aminudin Saputra Dimas Farian Savero Donaya Pasha Dwi Feriyanto Ega Budiman Elin Mayoana Fitri Erliyan Redi Susanto Erliyan Redy Susanto Erliyan Susanto Fadly, Muhtad Fahlul Rizki Fahri Damarjati Ferly Ardhy Fernando, Yusra Galuh Eka Saputra Hadibrata, Exsa Hari Soetanto Heni Sulistiyani Hironimus Edit Kristanto Ikna Awaliyani Imam Ahmad Imam Alkarim Jafar Fakhrurozi Jayawarsa, A.A. Ketut Jhonnry Frengky Bire Logo Keith Francis Ratumbuisang Khairun Nisa Kisworo Kisworo Kurnia Muludi Lutfy, Azza’zunda Choibar Lyla Putri Deviana Mardha Ariyani Masdiana Masdiana Mehta, Abhishek R Merriam Listiany Modeong Monica Efniasari Mufid Aden Muhamad Fitratullah Muhammad Zihad Prasetyo Mutiara Bulan Maharani Nanda Putra Wicaksono Nisa Berawi, Khairun Nur Aminudin Parjito Parjito Permata Permata, Permata Permatan Priandika, Adhie Thyo Purnama, Citra Andini Putra Syahwal Alam Rachmat Setiabudi Rahmandini, Hafizhah Harjiati Redi Ari Saputra Redy Susanto, Erliyan Rohmah, Nurbaiti Rusliyawati Ryan Randy Suryono Sampurna Dadi Rizkiono Sanriomi Sintaro Saputra, Dani Setiawansyah Setiawansyah Siska Narulita Sri Ratna Sulistiyanti suaidah suaidah Susanto, Erliyan Redy Sutyarso Sutyarso Sutyarso Sutyarso Suyahman Suyahman Syazili Mustofa Tahta Herdian Andika Tien Yulianti Trisnawan, Ahmad Budi Verdian, Arry Very Dwi Setiawan Very Hendra Saputra Wahyu Caesarendra Wahyu Caesarendra Wamiliana Wamiliana Waqas Arshad, Muhammad Warsito Warsito Widiastuti, Rosalina Yani Wildani Hakim Yana Ayu, Dwi Yodhi Yuniarthe YOHANA TRI UTAMI, YOHANA TRI Yudistira Yudistira Yulaikha Mar’atullatifah Yuri Rahmanto Zulkifli