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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 Indonesian Journal of Applied Informatics 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) TIN: TERAPAN INFORMATIKA NUSANTARA 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 Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering 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 Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Implementasi Web Klasifikasi Suasana Hati Berdasarkan Potongan Lagu dengan Memanfaatkan Convolutional Neural Network Roy Fasti; Donny Avianto
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 1 (2024): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i1.573

Abstract

Music is often used to accompany the user according to his heart condition. So users often create a playlist by adjusting the mood they are feeling. However, there are some users who have had difficulties in making playlists because in making a playlist it has to be done manually, that is, listening to music one at a time, wasting a lot of time. Therefore, the author conducted research on the classification of the mood contained in music and created a system that works to help classify music automatically by using one method that is part of deep learning, the method mentioned by the author is the Convolutional Neural Network (CNN) method. As for the data used by the investigator in this study is music data with a lot of data amounting to 400 data, on such data is done preprocessing data by cutting the duration of music and converting music into image. The next step is to split the data, dividing it into training data and test data. The training data is divided by 80% and the test data is also split by 20% of the total datasets used by the author. The results of this data division were used to build a model using the CNN model. The accuracy results obtained in this study were 95% for the training accurately and 68% for the data validation accurate.
Sistem Pakar Diagnosa Kelainan Stunting Balita Menggunakan Metode KNN Berbasis Web Amalia Rizki Wulandari; Donny Avianto
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 1 (2024): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i1.587

Abstract

Stunting in young children is a child nutrition problem in Indonesia. This research has the main objective of preventing and overcome it through a technique that can predict stunted babies based on data or information. This study using KNN method uses 125 test data and the value of k = 3 gives an accuracy of 96%. This includes 119 accurate predictions and 6 inaccurate predictions. The aim of this study is to predict the outcome of stunting in toddlers. If they fall into the stunting category, the parents of the toddler must pay more attention to the nutritional development of the toddler to be able to minimize the stunting rate in Indonesia.
Perbandingan Metode K-Nearest Neighbor dan Support Vector Machine Untuk Memprediksi Penerima Beasiswa Keringanan UKT Enggar Novianto; Arief Hermawan; Donny Avianto
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.6913

Abstract

Scholarships are financial assistance provided to individuals, pupils, or scholars to extend their education. These may be provided by government agencies or the colleges themselves to students. One component that ensures quality human resources is formal education. The purpose of scholarships is to help disadvantaged or underprivileged students. Scholarship providers usually give some consideration to the student's level of difficulty, such as parents' salary and number of siblings. Due to the large number of applications for relief scholarships and strict assessment criteria, not all students who apply can be accepted. Scholarship application selection officers often have difficulty determining which students are worthy of receiving a scholarship. While the quota for scholarship recipients for this study program is always limited, applications for student UKT relief scholarships continue to increase every semester. This application came from students with poor economic conditions. To select UKT relief scholarship application documents, you have to consider various criteria and use manual methods which are less effective and require more time to determine the results. This research aims to make a comparison between the K-Nearest Neighbor and Support Vector Machine classification algorithms in determining recipients of UKT relief scholarships for undergraduate students in the Legal Sciences Study Program, Faculty of Law, Sebelas Maret University using the RapidMiner application. The accuracy results obtained using the RapidMiner application that have been carried out, the K-NN method produces an accuracy of 92.92%, while the SVM method produces an accuracy of 85.84%, so the K-NN method is the best method in classification for predicting recipients of UKT relief scholarships for students in the program. Bachelor of Law studies.
KLASIFIKASI ALGORITMA K-NEAREST NEIGHBOR, NAIVE BAYES, DECISION TREE UNTUK PREDIKSI STATUS KELULUSAN MAHASISWA S1 Enggar Novianto; Arief Hermawan; Donny Avianto
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 8 No 2 (2023): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v8i2.3434

Abstract

Students are a crucial factor that must be considered in seriously evaluating study programs. The indicator of the success of the study program is the length of time it takes to complete the study. The study period is the time when students complete their studies. In addition, student study time reflects the level of student learning performance. In a broader perspective, the average student study time affects the quality of study programs and therefore student study time is used as one of the criteria in determining the assessment by the National Accreditation Board for Higher Education (BAN PT). The purpose of this study was to understand how well the K-Nearest Neighbor, Naive Bayes, Decision Tree performed to predict undergraduate students of the Law Study Program, Faculty of Law, Sebelas Maret University, graduating on time using the RapidMiner application. From the results of the testing and prediction process with the RapidMiner application using the three methods that have been carried out. The K-Nerest Neighbor (KNN) method obtained an accuracy of 96.67%, in the prediction test using the Naïve Bayes method it obtained an accuracy of 77.33%, while the Decision Tree method obtained an accuracy of 94.00%. So that the K-NN method is the best method in comparative classification in predicting student graduation on time with a predicted accuracy value of 96.67%.
Optimization of Hyperparameter K in K-Nearest Neighbor Using Particle Swarm Optimization Muhammad Rizki; Arief Hermawan; Donny Avianto
JUITA: Jurnal Informatika JUITA Vol. 12 No. 1, May 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i1.20688

Abstract

This study aims to enhance the performance of the K-Nearest Neighbors (KNN) algorithm by optimizing the hyperparameter K using the Particle Swarm Optimization (PSO) algorithm. In contrast to prior research, which typically focuses on a single dataset, this study seeks to demonstrate that PSO can effectively optimize KNN hyperparameters across diverse datasets. Three datasets from different domains are utilized: Iris, Wine, and Breast Cancer, each featuring distinct classification types and classes. Furthermore, this research endeavors to establish that PSO can operate optimally with both Manhattan and Euclidean distance metrics. Prior to optimization, experiments with default K values (3, 5, and 7) were conducted to observe KNN behavior on each dataset. Initial results reveal stable accuracy in the iris dataset, while the wine and breast cancer datasets exhibit a decrease in accuracy at K=3, attributed to attribute complexity. The hyperparameter K optimization process with PSO yields a significant increase in accuracy, particularly in the wine dataset, where accuracy improves by 6.28% with the Manhattan matrix. The enhanced accuracy in the optimized KNN algorithm demonstrates the effectiveness of PSO in overcoming KNN constraints. Although the accuracy increase for the iris dataset is not as pronounced, this research provides insight that optimizing the hyperparameter K can yield positive results, even for datasets with initially good performance. A recommendation for future research is to conduct similar experiments with different algorithms, such as Support Vector Machine or Random Forest, to further evaluate PSO's ability to optimize the iris, wine, and breast cancer datasets.
A Comparison of Product Weight Method and Simple Addition Weight Method in Employee Selection System Alwani, Adie G.; Avianto, Donny
International Journal Software Engineering and Computer Science (IJSECS) Vol. 3 No. 3 (2023): DECEMBER 2023
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v3i3.1826

Abstract

Tegar Mandiri Steam Ironing Service is a business operating in the steam ironing service industry, located in the Boyolali area, specifically in Dlingo Village, Mojosongo Subdistrict, Boyolali Regency, Central Java. "Tegar Mandiri" still employs conventional methods in the selection and recruitment of employees without leveraging technology. Although the implementation of conventional employee recruitment has been smooth, several issues have arisen. For instance, prospective applicants must visit in person to find out about job vacancies. Furthermore, the registration process for potential employees is still manually executed, requiring individuals to physically visit the site. Consequently, the administrative staff must record each applicant's details individually, leading to an ineffective data collection process and vulnerability to data loss or damage in physical records. The implementation phase was conducted through coding in PHP, adopting the Simple Additive Weighting (SAW) and Weight Product (WP) methods. Although both methods exhibit minor differences in their comparison, the results tend to favor the SAW method. The accuracy level of the SAW method reaches 96% as it has advantages over the WP method in terms of data normalization. The SAW method can address issues related to scale and unit differences among various criteria. Therefore, the SAW method is more accurate and dominant in determining the priority of employee recruitment
Optimization of K Value in KNN Algorithm for Spam and HAM Classification in SMS Texts Apriansyah, Ferryma Arba; Hermawan, Arief; Avianto, Donny
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 2 (2024): AUGUST 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i2.2681

Abstract

Spam refers to the unsolicited and repetitive sending of messages to others via electronic devices without their consent. This activity, commonly known as spamming, is typically carried out by individuals referred to as spammers. SMS spam, which often originates from unknown sources, frequently contains advertisements, phishing attempts, scams, and even malware. Such spam messages can be pervasive, affecting almost all mobile phone numbers, thereby causing significant disruptions to communication by delivering irrelevant content. The persistent nature of spam messages underscores the need for effective filtering mechanisms. This study investigates the application of the K-Nearest Neighbors (KNN) algorithm for classifying SMS messages as either spam or non-spam (ham). The findings demonstrate that KNN, when optimized through various methods for determining the appropriate value of K, can achieve an impressive average accuracy of 99.16% in classifying SMS spam. This high level of accuracy indicates that KNN is a reliable method for spam detection.
Peningkatan keterampilan komputer bagi Siswa SDN 1 Sinduadi Sleman Ratnawati, Dwi; Tri Untoro, Iwan Hartadi; Vivianti, Vivianti; Hardiyantari, Oktavia; Fatmawati, Soraya; Widodo, Tri; Avianto, Donny
KACANEGARA Jurnal Pengabdian pada Masyarakat Vol 6, No 3 (2023): Agustus
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/kacanegara.v6i3.1615

Abstract

Perkembangan teknologi informasi dan komunikasi mengusai semua bidang, terutama bidang Pendidikan. Kegiatan ujian berbasis paperless diwajibkan pemerintah dilakukan oleh semua sekolah. Banyak siswa yang belum memiliki kemampuan dalam komputer dasar sehingga mereka kesulitan dalam mengerjakan ujian dalam bentuk paperless. Permasalahan tersebut diselesaikan dengan mengadakan pelatihan Peningkatan Keterampilan Komputer bagi Siswa SD N 1 Sinduadi, Sleman yang dilakukan selama dua minggu. Pelaksanaan ini dilakukan dengan pendampingan dari tim dosen Universitas Teknologi Yogyakarta. Kegiatan ini dilakukan untuk mengatasi kesulitan siswa dalam mengoperasikan komputer dasar. Hasil dari pelaksaan ini adalah  78% siswa mampu meningkatkan kompetensi mengoperasikan komputer dasar dengan baik, dan 22% belum dapat menguasai komputer dasar dengan baik
Analyze Important Features of PIMA Indian Database For Diabetes Prediction Using KNN Perdana, Aziz; Hermawan, Arief; Avianto, Donny
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 1 (2023): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i1.1598

Abstract

Diabetes is a chronic, non-communicable disease, and a long-term health condition that affects how the body uses glucose, the type of sugar that gives energy. In Indonesia, diabetes ranks as the sixth highest cause of death, following conditions related to childbirth. In 2021, Indonesia has a total of 19.5 million diabetes patients, making it the fifth-highest in the world. Some machine learning research has used data from the PIDD (PIMA Indian Diabetes Dataset) to predict diabetes. In this research, in addition to prediction accuracy, data complexity is also important. This research analyzes important features in the PIMA Indian database using the KNN (k-nearest neighbor) method for classification. The results show that using KNN with k=22 value results in the highest accuracy of 83.12%. The analysis also found that the important features required by the KNN method to achieve high accuracy from the PIMA Indian database, in order of importance, are glucose, age, insulin, blood pressure, Body Mass Index, pregnancy, skin thickness, and diabetes pedigree function. However, when used in the KNN classification method, the diabetes pedigree function feature was found to be unnecessary, not relevant, and can be reduced. 
Klasifikasi Penyakit Antraknosa Pepaya California Menggunakan Convolutional Neural Network Nurazila, Siti; Avianto, Donny
JTERA (Jurnal Teknologi Rekayasa) Vol 8, No 1: June 2023
Publisher : Politeknik Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31544/jtera.v8.i1.2022.165-174

Abstract

Pepaya memiliki berbagai varian, salah satunya adalah pepaya California yang memiliki nilai jual tinggi di pasaran. Namun, petani sering kali mengalami gagal panen dikarenakan munculnya penyakit pada pepaya California. Salah satu penyakit yang menyerang pepaya adalah penyakit antraknosa. Kurangnya pengetahuan petani, apalagi petani baru sangat berpengaruh dengan kurangnya tindakan pencegahan penyakit antraknosa. Oleh karena itu, dibuatlah penelitian klasifikasi penyakit antraknosa pada pepaya California menggunakan Convolutional Neural Network (CNN). Data penelitian yang digunakan berjumlah 300 data citra dengan pembagian 150 data pepaya sehat dan 150 data pepaya antraknosa. Dalam proses pembangunan model CNN dataset akan dibagi menjadi dua bagian dengan perbandingan 80:20 antara data training dan data validation. Penelitian ini bertujuan untuk memberi informasi kepada petani baru tentang pengklasifikasian penyakit antraknosa pada pepaya. Hasil pengujian menunjukkan bahwa model terbaik dihasilkan menggunakan parameter optimizer Adam, epoch 20, dan loss binary cross-entropy. Model tersebut menghasilkan akurasi training 99,17% dan testing 99,58% dengan loss training 0,0239 dan loss validation 0,0177. Hasil penelitian menunjukkan bahwa algoritma CNN optimal dalam melakukan klasifikasi citra pepaya.
Co-Authors Adhitama, Satriya Adicahya, Bina Sukma Adityo Permana Wibowo 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 Bimantoro, Nazar Iqbal 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 Iin Rohmatika Aulia Ilmy Eka Handayani Imantoko Imantoko Indra Maulana Iqbal, Muhammad Izza Jagad Raya Ramadhan Kurniawan, Dimas Rizqi Kusban, Muhammad Kusumastuti, Asriana Dyah Maulana, Adha Muh Arifandi Muhammad Irsyad Indra Fata Muhammad Rizki Muhammad Rizki Nasmah Nur Amiroh Novaldy, Olwin Kirab Nur Widiastuti Nurazila, Siti Octavianus, Yonathan Perdana, Aziz Purba, Yurjaa Ghoniyyan Purnomo Pratama, Rizki Putra, Kristianto Pratama Dessan Rahma Nur Azizah 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