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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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Segmentasi Nasabah Kartu Kredit Berdasarkan Pola Transaksi untuk Penentuan Profil Nasabah Budiyanto, Irfan; Hermawan, Arief; Avianto, Donny
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 3 (2025): Oktober 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i3.1669

Abstract

Segmentasi nasabah kartu kredit penting untuk optimasi strategi pemasaran dan personalisasi layanan. Penelitian ini mengusulkan sistem segmentasi nasabah berdasarkan pola transaksi, yaitu frekuensi dan nilai transaksi, menggunakan algoritma K-Means Clustering. Dataset dari Kaggle, yang telah melalui tahap preprocessing, digunakan untuk mengidentifikasi cluster optimal. Metode Elbow dan Silhouette digunakan untuk menentukan jumlah cluster, dan keduanya mengindikasikan jumlah cluster optimal sebanyak 3, dengan titik siku pada grafik inersia di k=3 dan skor Silhouette tertinggi juga di k=3.  Hasilnya, terdapat tiga cluster nasabah: nasabah aktif tarik tunai (ditandai dengan tingginya penggunaan cash advance), nasabah pasif (dengan frekuensi dan nilai transaksi rendah), dan nasabah aktif transaksi pembelian (dengan aktivitas pembelian tinggi dan penggunaan cash advance rendah). K-Means terbukti efektif dalam membagi nasabah menjadi tiga cluster berbeda ini.  Segmentasi ini memungkinkan strategi pemasaran yang lebih tertarget, seperti penawaran produk finansial yang relevan untuk setiap cluster, dan pada akhirnya dapat meningkatkan kepuasan nasabah serta profitabilitas.
Learning Accuracy with Particle Swarm Optimization for Music Genre Classification Using Recurrent Neural Networks Rizki, Muhammad; Hermawan, Arief; Avianto, Donny
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : 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.
A HYBRID ARIMA-MLP ALGORITHM USING ARIMA AND MLP TO IMPROVE ESTIMATION MODEL PERFORMANCE IN SOLAR RADIATION SENSOR DATA Syahab, Alfin Syarifuddin; Hermawan, Arief; Avianto, Donny
IJISCS (International Journal of Information System and Computer Science) Vol 7, No 3 (2023): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v7i3.1617

Abstract

Ground-based solar radiation measurements help solar energy projects and applications. Various models have been developed to estimate solar radiation. Then, several additional models were created using improved machine learning. Currently, estimating solar radiation with the help of hybrid models is more efficient. In this research, the basic concepts of modeling procedures for hybrid between the Autoregressive Integrated Moving Average (ARIMA) and the Multilayer Perceptron (MLP) are used to improve the performance of the ARIMA and MLP models in estimating solar radiation data from a pyranometer sensor installed on the automatic weather station (AWS) at Stasiun Klimatologi Daerah Istimewa Yogyakarta.  The test results of the estimation model based on the coefficient of determination (R2) value and root mean square error (RMSE) show that the ARIMA model can provide a high coefficient of determination value in each data splitting scenario. The MLP estimation model shows a coefficient of determination value that is lower than the ARIMA model. On the other hand, MLP is able to improve the RMSE value in the ARIMA model in 70:30 and 90:10 splitting data. Furthermore, the ARIMA-MLP hybrid estimation model is able to improve the RMSE value of the ARIMA and MLP models even though the coefficient of determination value is not as good as the ARIMA model. This research shows that the ARIMA-MLP hybrid model is able to contribute to increasing the accuracy value in RMSE compared to the ARIMA and MLP models in estimating solar radiation sensor data.
Media Pembelajaran Aksara Jawa untuk Anak Sekolah Dasar Menggunakan Augmented Reality Hanif, Rifqi Fadhlurrahman; Avianto, Donny
Jurnal Informatika Universitas Pamulang Vol 9 No 1 (2024): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v9i1.39598

Abstract

Learning the Javanese script used in the Javanese language is challenging, especially for those who are just starting to learn the language. Students and the general public rarely use or are interested in Javanese script because of its difficulty. In order to protect the local language, researchers came up with the idea of teaching Javanese script to students, especially students who are just starting to learn in elementary school. This is done by creating a learning media application that is interesting, easy to understand, and entertaining. This Javanese script learning application is made using Augmented Reality (AR) technology and penanda-based tracking mechanism. This program is a learning tool that can turn 2D objects into 3D objects and bring the virtual world into the real world because it uses Augmented Reality technology. When used with the app's quiz feature, this educational tool can help elementary school students to improve their memory and skills, as well as inspire them to learn more through engaging instructions. The app was tested through blackbox testing to demonstrate its feasibility, with 100% successful results for all intended buttons and functions. AR camera distance testing resulted in a 90% success rate in detecting penandas at a certain distance.
RECOGNITION OF REAL-TIME HANDWRITTEN CHARACTERS USING CONVOLUTIONAL NEURAL NETWORK ARCHITECTURE Gumilang, Muhammad Satrio; Avianto, Donny
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 5 (2023): JUTIF Volume 4, Number 5, October 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.5.993

Abstract

Pattern recognition, including handwriting recognition, has become increasingly common in everyday life, as is recognizing important files, agreements or contracts that use handwriting. In handwriting recognition, there are two types of methods commonly used, namely online and offline recognition. In online recognition, handwriting patterns are associated with pattern recognition to generate and select distinctive patterns. In handwritten letter patterns, machine learning (deep learning) is used to classify patterns in a data set. One of the popular and accurate deep learning models in image classification is the convolutional neural network (CNN). In this study, CNN will be implemented together with the OpenCV library to detect and recognize handwritten letters in real-time. Data on handwritten alphabet letters were obtained from the handwriting of 20 students with a total of 1,040 images, consisting of 520 uppercase (A-Z) images and 520 lowercase (a-z) images. The data is divided into 90% for training and 10% for testing. Through experimentation, it was found that the best CNN architecture has 5 layers with features (32, 32, 64, 64, 128), uses the Adam optimizer, and conducts training with a batch size of 20 and 100 epochs. The evaluation results show that the training accuracy is between 85, 90% to 89.83% and testing accuracy between 84.00% to 87.00%, with training and testing losses ranging from 0.322 to 0.499. This research produces the best CNN architecture with training and testing accuracy obtained from testing. The developed CNN model can be used as a reference or basis for the development of more complex handwriting pattern recognition models or for pattern recognition in other domains, such as object recognition in computer vision, facial recognition, and other object detection.
Fuzzy Mamdani for Equality of Employee Salary Fakharudin, Panji Rangga Adzan Fajar; Avianto, Donny
Sistemasi: Jurnal Sistem Informasi Vol 13, No 1 (2024): 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.v13i1.3621

Abstract

Every job in a coffee shop has a salary. Salary is a form of recognition or imbalance for the results achieved. Salary is often also called wages, which is an imbalance in the services provided regularly to employees. Fuzzy has several methods, one of which is Fuzzy Mamdani which is used to make inferences or take the best decision in a problem that has subtle values. In 1975, Ebrahim Mamdani proposed the Fuzzy Mamdani method. Fuzzy Mamdani is a method that uses linguistic rules and has a fuzzy algorithm so that it can be explained mathematically and is easy to understand. The input values for the criteria for length of work are 8, experience 8, and dependents 4. The output is based on Fuzzy Mamdani's calculations, the employee gets a salary of IDR 2.21 million. The results of this study research that the wage income offered by Warkop IN`DA to its employees is good and not far from the minimum wage of the coffee shop. This third variable has a big influence on the final calculation results. The system created is quite good because the MAE result is 0.567 and the MAPE result is 36.720%.
Implementasi Speech Recognition Menggunakan Long Short-Term Memory untuk Software Presentasi Adhitama, Satriya; Avianto, Donny
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.6950

Abstract

Presentation is one of the methods for delivering thoughts, ideas, and concepts to an audience verbally. Presentation activities can be supported by presentation software that can be used to organize the sequence of material to be presented with visually appealing visuals. Operating presentation software requires technical assistance such as a remote, mouse, keyboard, and even a personal assistant, which can be distracting to the presenter as it limits their freedom in delivering the material. This distraction can be addressed through the implementation of speech recognition as a command to operate presentation software, making it easier for the presenter. A speech recognition system is developed using Long Short-Term Memory (LSTM), which can handle the issues of long-term dependency and vanishing gradient associated with Recurrent Neural Networks (RNN). There are 10 command words used to operate the presentation software. LSTM demonstrates superior performance when compared to alternative techniques like DNN, CNN, and SimpleRNN, achieving a training accuracy of 96.5%, a validation accuracy of 94.8%, and a testing accuracy of 94%. The LSTM method can be effectively used for sequential data to recognize real-time speech.
PENGENALAN CITRA RAMBU LALU LINTAS MENGGUNAKAN EKSTRAKSI FITUR MOMENWARNA DAN K-NEAREST NEIGHBOR Rizarta, Rusma Eko Fiddy; Avianto, Donny
Computatio : Journal of Computer Science and Information Systems Vol. 3 No. 1 (2019): COMPUTATIO : JOURNAL OF COMPUTER SCIENCE AND INFORMATION SYSTEMS
Publisher : Faculty of Information Technology, Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/computatio.v3i1.4272

Abstract

The traffic signs are signs with specific shape and symbols, letters, numbers, or words which have the aim to warn or inform the road users. However, there are many road users who are not aware of the meaning of each signs. In this research, we develop an application which can classify a road sign image into three classes, priority four-way crossroad, do-not-park sign, and follow-this-road sign. Initially, the system will do preprocessing step such as grays calling, histogram equalization, and input image segmentation. Next, the feature extraction step will be conducted, namely the spatial moment feature extraction, normalized centering, and color statistics. Lastly, the feature representation from both extraction methods will be used to classify the image using K-nearest neighbor. Experiment result shows that the combination of both feature extraction methods gives promising result. From 21 training images and 15 testing images, the system can recognize the traffic signs with 100% accuracy with K=3, 86.6% with K=5, and 86.6% with K=7. Rambu lalu lintas merupakan salah satu alat perlengkapan jalan dalam bentuk tertentu yang memuat lambang, huruf, angka, kalimat yang digunakan untuk memberikan perintah, larangan, peringatan dan petunjuk bagi pengguna jalan agar tertib berlalu lintas. Namun, banyak di antara pengguna jalan yang belum mengetahui arti dari setiap rambu lalu lintas yang terpasang.Pada penelitian ini, dibuatlah suatu aplikasi yang mampu melakukan klasifikasi citra rambu ke dalam 3 kelas yaitu: peringatan simpang empat prioritas, larangan parkir dan perintah memasuki jalur atau lajur yang ditunjuk. Mula-mula sistem akan melakukan prapemrosesan seperti seperti: grayscalling, histogram equalization, dan segmentasi pada citra input. Selanjutnya, tahap ekstraksi ciri akan dilakukan pada citra hasil pra-pemrosesan. Adapun metode ekstraksi ciri yang digunakan pada penelitian kali ini adalah ekstraksi fitur momen spasial dan pusat ternormalisai (momen) dan ekstraksi fitur statistika warna (warna). Terakhir, nilai fitur yang dihasilkan oleh kedua metode tersebut akan diklasifikasi mengguakan K-Nearest Neighbor. Hasil uji coba menunjukkan bahwa metode ekstraksi fitur gabungan momen-warna memberikan hasil yang menjanjikan. Dari 21 citra latih dan 15 citra uji yang digunakan, sistem mampu mengenali rambu dengan tepat 100% pada K=3 , 86,6% pada K=5, dan 86,6% pada K=7. 
Analisis Komparasi Algoritma K-Means Dan K-Medoids Dalam Segmentasi Data Untuk Strategi Promosi Mahasiswa Baru Di Universitas X Syafrudin, Teguh; Teguh Syafrudin; Arief Hermawan; Donny Avianto; Indra Maulana
Komputika : Jurnal Sistem Komputer Vol. 14 No. 2 (2025): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v14i2.16698

Abstract

Persaingan dalam merekrut mahasiswa baru semakin ketat, sehingga perguruan tinggi memerlukan strategi promosi yang efektif dan tepat sasaran. Salah satu cara untuk meningkatkan efektivitas promosi adalah dengan melakukan segmentasi calon mahasiswa berdasarkan data penerimaan. Penelitian ini menawarkan solusi dengan membandingkan performa algoritma K-Means dan K-Medoids dalam segmentasi data Penerimaan Mahasiswa Baru (PMB) Universitas X tahun 2024. Metode yang digunakan meliputi tahapan pengumpulan data, preprocessing (pembersihan, normalisasi, dan transformasi), implementasi algoritma K-Means dan K-Medoids, serta evaluasi kualitas klaster menggunakan Davies-Bouldin Index (DBI). Hasil penelitian menunjukkan bahwa konfigurasi tiga klaster (K=3) memberikan nilai DBI terendah, dengan K-Medoids mencapai 1,038, lebih baik dibandingkan K-Means sebesar 1,059. Klaster dominan menunjukkan bahwa lulusan SMK mendominasi sebesar 40,45% dan cenderung memilih program studi Pendidikan TIK. Kontribusi penelitian ini adalah memberikan panduan bagi institusi pendidikan dalam memilih algoritma klasterisasi yang paling sesuai untuk mendukung strategi promosi yang lebih akurat, efisien, dan terarah.
ANALISIS KINERJA SUPPORT VECTOR MACHINE DAN NAÏVE BAYES CLASSIFIER DALAM KLASIFIKASI SENTIMEN DAN EMOSI PADA UMPAN BALIK MAHASISWA TERHADAP KINERJA DOSEN Syafrudin, Teguh; Hermawan, Arief; Avianto, Donny
Jurnal Digit : Digital of Information Technology Vol 15, No 2 (2025)
Publisher : Universitas Catur Insan Cendekia (CIC) Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51920/jd.v15i2.442

Abstract

Evaluasi kinerja dosen melalui umpan balik mahasiswa merupakan komponen penting dalam meningkatkan mutu pembelajaran di perguruan tinggi. Penelitian ini bertujuan untuk mengembangkan model klasifikasi sentimen dan emosi menggunakan pendekatan hybrid machine learning dengan mengombinasikan algoritma Naïve Bayes dan Support Vector Machine (SVM). Dataset berasal dari umpan balik mahasiswa yang telah dianotasi secara manual ke dalam tiga kategori sentimen positif, netral, negatif dan delapan kategori emosi. Proses preprocessing dilakukan melalui tokenisasi, stemming, dan transformasi data ke dalam bentuk TF-IDF. Hasil klasifikasi menunjukkan bahwa SVM memiliki performa terbaik untuk klasifikasi sentimen dengan akurasi mencapai 90%, mengungguli Naïve Bayes yang hanya memperoleh akurasi 80%. Sebaliknya, performa klasifikasi emosi jauh lebih rendah, dengan akurasi maksimum 35% pada model SVM dan 20% pada Naïve Bayes. Beberapa emosi seperti “marah”, “termotivasi”, dan “senang” tidak dapat dikenali oleh model karena ketidakseimbangan distribusi data dan konteks emosi yang sulit ditangkap dari teks pendek. Temuan ini menunjukkan bahwa pendekatan hybrid efektif untuk klasifikasi sentimen dalam kondisi data terbatas, namun klasifikasi emosi memerlukan pendekatan lanjutan seperti reduksi label atau penggunaan model berbasis konteks untuk mencapai hasil yang lebih baik.Kata kunci: Kinerja Dosen, Naïve Bayes Classifier (NBC), Support Vector Machine (SVM), Umpan Balik Mahasiswa.
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