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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Buana Informatika Jurnal Informatika Jurnal Teknologi Informasi dan Ilmu Komputer JUITA : Jurnal Informatika Jurnas Nasional Teknologi dan Sistem Informasi POSITIF Edu Komputika Journal Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Computatio : Journal of Computer Science and Information Systems RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Khatulistiwa Informatika JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri JTERA (Jurnal Teknologi Rekayasa) Jurnal Sains dan Informatika INOVTEK Polbeng - Seri Informatika Matrix : Jurnal Manajemen Teknologi dan Informatika SINTECH (Science and Information Technology) Journal Jurnal Informatika Universitas Pamulang Jurnal Teknoinfo Jurnal Sisfokom (Sistem Informasi dan Komputer) KACANEGARA Jurnal Pengabdian pada Masyarakat MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer KOMPUTIKA - Jurnal Sistem Komputer KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Jurnal Riset Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Terapan Jurnal Teknologi Terpadu EDUMATIC: Jurnal Pendidikan Informatika EVOLUSI : Jurnal Sains dan Manajemen Building of Informatics, Technology and Science JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Teknologi Informasi dan Multimedia Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) JISA (Jurnal Informatika dan Sains) International Journal of Engineering, Technology and Natural Sciences (IJETS) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Idealis : Indonesia Journal Information System Jurnal Teknik Informatika (JUTIF) Jurnal Digit : Digital of Information Technology Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Science in Information Technology Letters Journal of Soft Computing Exploration Jurnal Indonesia : Manajemen Informatika dan Komunikasi Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer International Journal Software Engineering and Computer Science (IJSECS) Jurnal Sains dan Teknologi International Journal Science and Technology (IJST) Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Scientific Research, Education, and Technology Journal of Data Science Theory and Application NERO (Networking Engineering Research Operation) SmartComp Jurnal Indonesia : Manajemen Informatika dan Komunikasi Emitor: Jurnal Teknik Elektro IJISCS (International Journal of Information System and Computer Science)
Donny Avianto
Universitas Teknologi Yogyakarta
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences

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TOMATO CLASSIFICATION BASED ON VARIETY WITH RGB FEATURE EXTRACTION AND NAÏVE BAYES ALGORITHM Widyastuti, Evi; Hermawan, Arif; Avianto, Donny
IDEALIS : InDonEsiA journaL Information System Vol. 8 No. 1 (2025): Jurnal IDEALIS Januari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v8i1.3370

Abstract

Tomato is a fruit-category vegetable plant that is easy to cultivate in various locations. The diversity of tomato varieties, such as Red Zebra Tomato, Green Zebra Tomato, and Kumato, often makes rapid and accurate variety identification challenging. Misclassification can impact the selection of environmental conditions and pest or disease management, ultimately leading to suboptimal cultivation results. Currently, research primarily focuses on tomato shape, diseases, and ripeness levels, while cultivar classification based on color characteristics remains limited. This study aims to develop a method for classifying tomato cultivars based on RGB color features using the Naïve Bayes algorithm. The research was conducted by collecting 45 tomato images with similar shapes but different colors (red, green, and dark red). The research stages include RGB feature extraction, data rounding, splitting training and test data with a 70:30 ratio, and classification using Naïve Bayes. A re-evaluation was performed by removing specific color attributes to assess their impact on accuracy. This study is expected to support rapid and accurate tomato variety identification, enhance efficiency in modern agriculture, and expand the application of technology in the agricultural industry to achieve advanced, self-sufficient, and modern farming. The results show that the RGB feature extraction method and the Naïve Bayes algorithm can classify tomato cultivars with an accuracy of up to 78.57%. The RG color attributes have the most significant impact on accuracy, reaching 85.71%.
Metode Neural Network Dalam Prediksi Jumlah Penumpang Kereta Api Berbasis Web Adicahya, Bina Sukma; Wulandari, Sri; Avianto, Donny
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.6001

Abstract

The demand for railway transport in Java has increased along with the increasing population growth and increasingly complex mobility needs. Trains have become one of the main modes of transport due to their efficiency and reliability in travelling medium and long distances. However, there is a significant imbalance between ticket demand and train capacity availability, especially during holiday seasons, holidays, and weekends. To solve this problem, a simulation is needed to predict the number of passengers in the future using the Neural Network Backpropagation method. The implementation of this system resulted in a prediction accuracy rate of 80.33%, which provides PT Kereta Api Indonesia with an important tool to better manage schedules and capacity. It is hoped that this research can make a significant contribution to improving the company's operational efficiency, while providing a better experience for passengers. In addition, this research is also expected to provide stakeholders with greater insight into the dynamics of transport demand in Java, and help formulate more effective policies to support the growth of the public transport sector. With the results of this study, PT Kereta Api Indonesia is expected to develop optimal strategies in adjusting train capacity to the unstable passenger demand, while local governments can utilise this information to design policies that support the sustainability of transport services in Java.
Learning Accuracy with Particle Swarm Optimization for Music Genre Classification Using Recurrent Neural Networks Muhammad Rizki; Arief Hermawan; Donny Avianto
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3037

Abstract

Deep learning has revolutionized many fields, but its success often depends on optimal selection hyperparameters, this research aims to compare two sets of learning rates, namely the learning set rates from previous research and rates optimized for Particle Swarm Optimization. Particle Swarm Optimization is learned by mimicking the collective foraging behavior of a swarm of particles, and repeatedly adjusting to improve performance. The results show that the level of Particle Swarm Optimization is better previous level, achieving the highest accuracy of 0.955 compared to the previous best accuracy level of 0.933. In particular, specific levels generated by Particle Swarm Optimization, for example, 0.00163064, achieving competitive accuracy of 0.942-0.945 with shorter computing time compared to the previous rate. These findings underscore the importance of choosing the right learning rate for optimizing the accuracy of Recurrent Neural Networks and demonstrating the potential of Particle Swarm Optimization to exceed existing research benchmarks. Future work will explore comparative analysis different optimization algorithms to obtain the learning rate and assess their computational efficiency. These further investigations promise to improve the performance optimization of Recurrent Neural Networks goes beyond the limitations of previous research.
PREDIKSI LONJAKAN PENJUALAN TOKO RETAIL ONLINE SAAT HARBOLNAS DENGAN MODEL SARIMA Putra, Kristianto Pratama Dessan; Hermawan, Arief; Avianto, Donny
Jurnal Khatulistiwa Informatika Vol 13, No 1 (2025): Periode Juni 2025
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jki.v13i1.25071

Abstract

Peralihan proses transaksi dari konvensional ke online telah merambah ke berbagai sektor, salah satunya toko retail. Banyak toko retail yang telah membangun sarana penjualan secara online dan setiap harinya jumlah transaksi mengalami peningkatan. Dengan semakin meningkatnya jumlah transaksi maka diperlukan peningkatan layanan server untuk mengakomodir kebutuhan pengguna. Selain itu, adanya tanggal-tanggal kembar yang dijadikan HARBOLNAS juga sering menyebabkan jumlah transaksi melonjak dari hari biasanya. Apabila lonjakan transaksi tidak diimbangi dengan spesifikasi server yang mumpuni maka akan terjadi “lost of sales”. Oleh karena itu, perlu adanya sistem prediksi untuk memperkirakan kenaikan ataupun lonjakan transaksi untuk hari mendatang guna antisipasi kebutuhan server. Dalam penelitian ini, metode prediksi yang digunakan adalah model SARIMA dengan dataset primer dari salah satu perusahaan retail online di Indonesia. SARIMA dipilih karena dataset memiliki bersifat musiman akibat adanya HARBOLNAS di setiap tanggal kembar. Hasilnya menunjukan bahwa model SARIMA sukses memproses dataset dan memprediksi lonjakan transaksi untuk 30 hari ke depan dengan nilai uji evaluasi MAPE di angka 15,05%. Lebih lanjut, penelitian juga menyediakan hasil uji evaluasi dengan metode lainnya sebagai pembanding untuk penelitian lanjutan dengan metode ataupun parameter yang berbeda.
Max Depth Impact on Heart Disease Classification: Decision Tree and Random Forest Rian Oktafiani; Arief Hermawan; Donny Avianto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5574

Abstract

Results in heart disease classification that are inaccurate and have low accuracy can endanger the patient's life. Some parameters in the algorithm model also influence classification. This study compares the Decision Tree and Random Forest algorithms for heart disease. The influence of maximum depth on heart disease classification also has significant implications. If the maximum depth is not set correctly, the classification results can be inaccurate and lead to incorrect diagnoses. This study uses five data split schemes, namely 60%: 40%, 70%: 30%, 75%: 25%, 80%: 20%, 90%: 10% and tested with different max depth parameters, namely max depth = 3, 4, 5, 6, and 7. This research produces the best accuracy using the 90%:10% scheme and max depth = 7 with the best accuracy result using the Random Forest algorithm of 99.29% while the Decision Tree algorithm is 98.05%. Then the precision and recall value of the Random Forest algorithm is 99% while the Decision Tree is 98%. The results of computation time using Decision Tree are faster than using Random Forest with a computation time for training data of 0.0075 s, while the testing data are 0.009 s. In future research, research can be conducted on the effect of other parameters by testing using several data sets.
ANALISIS PROPERTI PROSPEK DAN NON-PROSPEK BERDASARKAN DATA IKLAN MENGGUNAKAN METODE K-MEANS DAN K-MEDOIDS Maulana, Adha; Avianto, Donny
Journal of Data Science Theory and Application Vol. 4 No. 1 (2025): JASTA
Publisher : LP3M Universitas Putra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32639/vg5bfb91

Abstract

The significant growth of the property industry market in the city of Yogyakarta. has attracted more attention from property companies. This is glanced at by property companies in bridging sales. However, property agency companies often experience losses in plotting advertising budgets. With this problem, the author offers a clustering system for prospective and non-prospective property spots using the K-Means and K-Medoids methods with the Forward Selection feature selection method. This study aims to allocate advertising budgets to targeted projects with great potential. The data used is primary data, namely IRSC data from company x in Yogyakarta with a range (January 2023–March 2024) totaling 212 records. Data processing uses the RapidMiner application with a data composition of 70% used for training data and 30% for testing data. This process produces a DBI value of 1.060 for the K-Medoids method without feature selection and 1.974 for the K-Medoids method using feature selection. The best method produced by the K-Means method using and without feature selection with a DBI value of 0.148.
Analisis Perbandingan Metode DES (Double Exponential Smoothing) dan WMA (Weighted Moving Average) dalam Peramalan Penjualan Laptop Gunawan, Asrul; Hermawan, Arief; Avianto, Donny
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 1 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i1.15314

Abstract

Rapid technological developments increase demand for electronic devices, especially laptops. Fluctuations in monthly sales are a challenge for companies in determining the optimal amount of inventory. The inability to predict market demand can disrupt inventory management and customer satisfaction. Therefore, accurate sales forecasting is essential for planning marketing and procurement strategies. This study compares two sales forecasting methods, namely Double Exponential Smoothing (DES) and Weighted Moving Average (WMA), to analyze the accuracy of each method. The results showed that the DES method has a better level of accuracy with an average MAPE value of 16.72%, compared to WMA which reached 21.22%. This study provides practical insights for companies in choosing the right forecasting method, in order to improve inventory management, product procurement strategies, and customer satisfaction
PENERAPAN ALGORITMA K-NEAREST NEIGHBOR UNTUK DETEKSI DINI STATUS GIZI PASIEN DEWASA Wijayanti, Dian; Hermawan, Arief; Avianto, Donny
POSITIF : Jurnal Sistem dan Teknologi Informasi Vol 10 No 2 (2024): Positif : Jurnal Sistem dan Teknologi Informasi
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/positif.v10i2.2255

Abstract

Assessing the nutritional status of adult patients is essential to gain a comprehensive understanding of their condition and assist healthcare workers in planning appropriate treatment. However, manual assessment is time-consuming and labor-intensive, especially when the number of patients exceeds the number of available healthcare workers. This can hinder the timely and accurate delivery of nutritional care. The K-Nearest Neighbor (KNN) algorithm is a commonly used method for nutritional status classification, particularly in toddlers, pregnant women, or for obesity classification in adults. The use of KNN for early detection of adult nutritional status remains rarely explored. This study applies the KNN algorithm to classify the nutritional status of adult patients using data from the Alamanda 1 ward and the ICU ward at Sleman Regional General Hospital, collected from January 2 to October 18, 2023. The dataset includes patient height, weight, and nutritional status. The algorithm was implemented using RapidMiner with odd k-values less than 20, and data splits of 90:10, 70:30, and 50:50 for training and testing. Results show that the optimal k-values for the highest accuracy were k = 1 and k = 3 using the 70:30 data split, both achieving an accuracy of 96.77%. The highest sensitivity, 97.61%, was also achieved at k = 3 with the same data split. The KNN algorithm demonstrates strong potential to be developed into an early detection system for assessing the nutritional status of adult patients in hospitals, supporting faster and more accurate nutritional care services
The Impact of Extreme Data Imbalance on Evaluation Metrics of Deep Learning Models for Loan Default Prediction Budiyanto, Irfan; Hermawan, Arief; Avianto, Donny; Kusban, Muhammad
Emitor: Jurnal Teknik Elektro Vol 25, No 2: July 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/emitor.v25i2.10719

Abstract

The growth of financial technology has made online loans more accessible, but it has also increased the risk of borrowers failing to repay. Developing a reliable system to predict loan defaults is therefore very important. A common problem in these predictions is an imbalance in the data – there are far fewer cases of loan defaults (the minority class) than loans that are paid back on time (the majority class). This imbalance can cause the prediction models to be biased. This research specifically investigates the effect of an extremely increased data imbalance ratio (from 1:170 to 1:33,612) on the evaluation metrics of a Deep Neural Network (DNN) model, particularly when using the Adaptive Synthetic Sampling (ADASYN) oversampling technique. The method used involves adopting a previous research approach that combines ADASYN to handle data imbalance and DNN for prediction, applied to an updated Lending Club dataset with a more severe level of imbalance. The results demonstrate a critical breakdown in key evaluation metrics. Compared to previous research, Accuracy remains high (0.9515) and Specificity is strong (0.9516). However, there is a catastrophic decrease in Precision to almost zero (0.0001), a very low Recall (0.1667), and a resulting F1-Score that is also nearly zero (0.0002). A visual analysis using Principal Component Analysis (PCA) reveals that this decline in Precision is caused by synthetic minority samples generated by ADASYN completely overlapping with the original majority cluster, leading to a massive number of false positives. In conclusion, ADASYN fails to maintain a usable performance level under extreme imbalance conditions, rendering the model ineffective for its intended purpose and highlighting the critical need for alternative strategies when dealing with severe minority class scarcity.
Klasifikasi Citra Produk Chiffon Cake Dengan Metode K-Nearest Neighbors Dan Grey Level Co-Occurrence Matrix Untuk Quality Control Novaldy, Olwin Kirab; Hermawan, Arief; Avianto, Donny
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 4: Agustus 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.124

Abstract

Chiffon cake adalah salah satu kue yang populer, dan kepuasan pelanggan sangat dipengaruhi oleh kualitas produk chiffon cake. Oleh karena itu, diperlukan sistem pengendalian kualitas yang efektif untuk mendeteksi chiffon cake yang cacat. Dalam penelitian ini, digunakan metode K-Nearest Neighbors (KNN) sebagai alat klasifikasi untuk mendeteksi produk chiffon cake yang cacat. Tujuan utama penelitian ini adalah membuat model sistem pengendalian kualitas yang dapat mengklasifikasikan chiffon cake secara otomatis ke dalam dua kategori: "lolos" dan "cacat." Sistem ini diharapkan dapat meningkatkan efisiensi proses produksi dan mengurangi kemungkinan produk cacat sampai ke tangan konsumen. Studi ini menggunakan KNN dan Gray Level Co-Occurrence Matrix (GLCM). Metode klasifikasi KNN bergantung pada pemilihan tetangga terdekat data untuk menentukan kategori kelasnya, sedangkan GLCM adalah teknik ekstraksi fitur yang digunakan untuk mengukur tekstur gambar dengan menganalisis hubungan antara dua piksel dalam orde kedua. Untuk melatih model KNN, studi ini menggunakan dataset yang diambil sendiri melalui pemotretan produk chiffon, kemudian diberi label "lolos" dan "cacat." Setelah melatih model, penulis melakukan pengujian dengan data uji untuk mengevaluasi kinerjanya. Hasil penelitian menunjukkan bahwa penerapan KNN memungkinkan pengklasifikasian chiffon cake dengan akurasi 90,4%. Validasi lebih lanjut dengan dataset yang lebih besar dan beragam diperlukan untuk memastikan bahwa model tetap robust dan dapat diandalkan dalam berbagai kondisi produksi.   Abstract Chiffon cake is one of the popular types of cake, and customer satisfaction is greatly influenced by the quality of the chiffon cake. Therefore, an effective quality control system is necessary to detect defective chiffon cakes. In this research, the K-Nearest Neighbors (KNN) method is used as a classification tool to detect defective chiffon cakes. The main objective of this study is to create a quality control system model that can automatically classify chiffon cakes into two categories: "Pass" and "Not Pass." This model is expected to increase production efficiency and reduce the risk of defective products reaching consumers. This study uses KNN and the Gray Level Co-Occurrence Matrix (GLCM). The KNN classification method determines the class category by selecting the data's nearest neighbors, while GLCM is a feature extraction method that measures image texture by analyzing the correlation between two pixels in the second order. To train the KNN model, this study used a dataset of manually photographed products, labeled as "lolos" and "cacat" After training the model, this study evaluates its performance using test data. The research results showed that the KNN application can classify chiffon cakes with an accuracy of 90.4%. Further validation with larger and more diverse datasets is recommended to enhance the model's robustness and applicability.
Co-Authors Adhitama, Satriya Adicahya, Bina Sukma Adityo Permana Wibowo Alfin Syarifuddin Syahab Alwani, Adie G. Amalia Rizki Wulandari Apriansyah, Ferryma Arba Ardiansyah, Diky Aribowo Aribowo Arief Hermawan Arieska Restu Harpian Dwika Arif Hermawan, Arif Ashari, Nadia Aziz Perdana Baiq Nurul Azmi Bowo Hirwono Budiyanto, Irfan Dewi, Amelia Citra Dian Wijayanti Dimas Dwi Kurniawan Dwi Ratnawati, Dwi Edi Priyanto Enggar Novianto Enggar Novianto Erfin Nur Rohma Khakim Fadhila, Arifa Farras Fadilah, Faiz Fahri Putra Herlambang Fakharudin, Panji Rangga Adzan Fajar Faqih, Allan Bil Febiansyah Annaufal Ahnaf Fauzi Ferdinandus Edwin Penalun Gumilang, Muhammad Satrio Gunawan, Asrul Hanif, Rifqi Fadhlurrahman Hardiyantari, Oktavia Herdy Andriksen Ilmy Eka Handayani Imantoko Imantoko Indra Maulana Iqbal, Muhammad Izza Jagad Raya Ramadhan Kusban, Muhammad Kusumastuti, Asriana Dyah Maulana, Adha Muh Arifandi Muhammad Irsyad Indra Fata Muhammad Rizki Muhammad Rizki Muhammad Rizki Nasmah Nur Amiroh Nazar Iqbal Bimantoro Novaldy, Olwin Kirab Nur Widiastuti Nurazila, Siti Octavianus, Yonathan Perdana, Aziz Purba, Yurjaa Ghoniyyan Purnomo Pratama, Rizki Putra, Kristianto Pratama Dessan Reski Noviana Rian Oktafiani Rian Oktafiani Rianto Rianto Rizarta, Rusma Eko Fiddy Rizky Samudra Falasyfa Roy Fasti Rubangi Rubangi Rudi, Rudiono Rusma Eko Fiddy Rizarta Saputra, Candra Heru Setiawan, Muhhamad Ajun Siti Rokhanah Soraya Fatmawati Sri Wulandari SRI WULANDARI Sutarman Sutarman Syafrudin, Teguh Syahab, Alfin Syarifuddin Teguh Syafrudin Tri Untoro, Iwan Hartadi Tri Widodo Vivianti Wahid, Ach. Nur Aqil Widyastuti, Evi