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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
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IMPLEMENTASI METODE NAÏVE BAYES UNTUK KLASIFIKASI DATA BLOGGER Nur Widiastuti; Arief Hermawan; Donny Avianto
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 8, No 3 (2023)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v8i3.3713

Abstract

Di era teknologi yang modern seperti saat ini peluang kerja sebagai blogger cukup banyak diminati. Para blogger me-manfaatkan situs blog baik yang gratis maupun berbayar untuk menulis artikel. Hal tersebut menyebabkan pengguna situs blog semakin meningkat. Diantara para blogger ada yang menjadi blogger professional dan ada juga yang menjadi blog-ger musiman untuk menulis artikel pada blog. Penelitian ini meneliti blogger mana yang masuk dalam kategori blogger professional atau blogger musiman. Penelitian ini mengklasifikasi data blogger yang diambil dari UCI Machine Learning dengan jumlah data sebanyak 100 data kemudian diuji menggunakan Metode Naïve Bayes. Adapun tool yang digunakan untuk penelitian adalah Rapidminer untuk mengklasifikasi blogger professional atau blogger musiman. Penelitian ini menghasilkan akurasi sebesar 76,27% atau meningkat 1,27 % dibandingkan penelitian sebelumnya dan hasil classification error sebesar 23,73%. Sedangkan class recall sebanyak 12 fold, hal ini dapat diartikan penelitian menggunakan correla-tion matrix dan cross validation dengan number of fold 12 menghasilkan nilai akurasi yang lebih baik dari penelitian sebelumnya.
Perbandingan Random Forest Regression dan Support Vector Regression Pada Prediksi Laju Penguapan Ferdinandus Edwin Penalun; Arief Hermawan; Donny Avianto
JURNAL FASILKOM Vol 13 No 02 (2023): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v13i02.4976

Abstract

Memprediksi laju penguapan memiliki manfaat yang luas dalam berbagai aplikasi seperti manajemen sumber daya air, pertanian, dan lingkungan hidup. Namun untuk mendapatkan data yang lengkap dan akurat dalam mempelajari laju penguapan memiliki tantangan tersendiri. Selain itu, rendahnya tingkat linieritas antara data laju penguapan dan faktor meteorologi lainnya di wilayah tropis dapat menyebabkan hasil prediksi yang bervariasi. Tujuan dari penelitian ini adalah memprediksi laju penguapan harian di Stasiun Klimatologi Yogyakarta dengan membandingkan kinerja dua model machine learning (ML) yaitu random forest regression (RFR) dan support vector regression (SVR) menggunakan data pengamatan meteorologi harian. Untuk meningkatkan akurasi prediksi, dilakukan optimasi hyperparameter menggunakan metode gridsearch cross-validation untuk mencari kombinasi hyperparameter terbaik. Hasil optimasi hyperparameter pada data training menunjukkan bahwa model RFR menghasilkan skor RMSE sebesar -0,67 sementara model SVR pada kernel RBF menghasilkan skor RMSE negatif sebesar -0,57. Evaluasi lebih lanjut dilakukan pada data testing dengan menggunakan kombinasi hyperparameter hasil optimasi model RFR menghasilkan nilai R2 sebesar 0,79 dan RMSE sebesar 0,56 sedangkan model SVR menghasilkan koefisien determinasi (R2) sebesar 0,81 dan RMSE sebesar 0,53. Berdasarkan hasil perbandingan kedua model dapat disimpulkan bahwa model SVR memiliki kinerja yang lebih baik dalam memprediksi laju penguapan harian. Penggunaan teknik prediksi dengan model ML untuk memprediksi laju penguapan dapat menjadi solusi untuk mengisi kekosongan data pengamatan meteorologi dan memiliki manfaat yang signifikan dalam bidang pertanian dan hidrologi. Penelitian selanjutnya dapat melibatkan pengembangan sistem informasi pemantauan dan pengelolaan sumber daya air yang lebih efektif dan efisien.
Klasifikasi Penyakit Antraknosa Pada Cabai Merah Teropong ”Inko Hot” Dengan Metode Convolutional Neural Network Donny Avianto; Ilmy Eka Handayani
SINTECH (Science and Information Technology) Journal Vol. 6 No. 2 (2023): SINTECH Journal Edition Agustus 2023
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v6i2.1377

Abstract

The red chili variety "inko hot" is a type of red chili that has a high economic value. Unfortunately, these red chili plants are often infected with anthracnose disease, which results in significant losses for farmers. Anthracnose is one of the major diseases infecting chili plants, potentially resulting in crop failure and losses of up to 80%. The purpose of this study is to develop a classification system to identify anthracnose disease in red chili fruit, using Convolutional Neural Network (CNN) method. In this experiment, 1500 data were used, of which 80% were used as training data and 20% as validation data. The best results of this experiment produced a model with an accuracy of 97% and a loss rate of 6.45%, by applying the Nadam optimization algorithm and going through 50 iterations (epochs). The model showed good performance with a prediction accuracy rate of 83.33%. The development of this classification system has significant potential in providing efficient solutions to recognize diseases in chili plants. Through continuous development, this system can be a valuable tool for farmers to increase crop productivity and reduce the negative impact of disease attacks on red chili peppers and other crops.
Market Basket Analysis Menggunakan Algoritma Apriori dan FP Growth untuk Menentukan Pola Pembelian Konsumen Reski Noviana; Arief Hermawan; Donny Avianto
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6304

Abstract

An increase in sales transaction data every day will cause a very large amount of product sales transaction data to be stored. In most cases, data relating to very large sales transactions are only stored and used for archival purposes, not exploited adequately. In retail marketing, association data mining is used to investigate product purchasing patterns. However, if the company does not know the customer's purchasing patterns, it can have impacts such as inappropriate marketing strategies, decreased customer retention, lost business opportunities, lack of personalization, tough competition, stock/production inefficiencies, loss of customer trust. Knowing consumer purchasing patterns, the company can develop sales strategies and make the right decisions. In this study using Market Basket Analysis using the Apriori Algorithm and FP-Growth to determine consumer buying patterns. The results of this study resulted in two itemset combinations. The first combination is that if the buyer buys yogurt and sausage, the buyer also buys whole milk. The resulting support value is 0.00147 (0.0147%), the confidence value is 0.255814 (25.58%) and the lift value is 1.61986. the second combination, namely if the buyer buys sausage (sausages) and rolls/buns (bread rolls), then also buys whole milk (milk), this combination produces a support value of 0.001136 (0.0113%), a confidence of 0.2125 (21.25%) and a lift of 1.34559 . In addition to the combination of the itemset produced in this study, it also measures computational speed in processing Groceries data for Market Basket Analysis. The computational speed produced by the Apriori Algorithm is 3.1765 seconds, while the FP-Growth algorithm is 0.15892 seconds. The difference in computational speed between the Apriori Algorithm and FP-Growth is 3.0176 seconds.
Analisis Sentimen Opini Pengguna Twitter Terhadap Tragedi Kanjuruhan Malang dengan Metode Support Vector Machine Fahri Putra Herlambang; Donny Avianto
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6332

Abstract

The Kanjuruhan tragedy on October 1, 2022, strongly impacted Indonesian football stadium safety. At the Kanjuruhan Stadium, a Persebaya vs. Arema FC match resulted in the deaths of 135 supporters. Due to the significant number of fatalities, there is ongoing debate regarding the responsible parties for the tragedy. Since there are expected to be 18.45 million active users in Indonesia by 2022, Twitter research helps determine popular attitudes. Support Vector Machine is used in this work to evaluate tweets and identify whether they include positive or negative emotions. The categorization outcomes may influence how the public views those responsible for the tragedy. On October 6, 2022, specific Twitter data on tear gas riots, oppressive government, rivalry between supporters, and violence against authorities were taken into account. The sentiment classes are negative, neutral, and positive. The study attained a 95.55% f1-score, 95.16% accuracy, 97.56% precision, and 95.16% recall.
Implementasi Metode Naïve Bayes untuk Klasifikasi Penderita Penyakit Jantung Bowo Hirwono; Arief Hermawan; Donny Avianto
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 7 No 3 (2023): JULY-SEPTEMBER 2023
Publisher : KITA Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v7i3.910

Abstract

Heart attack is a very serious heart disorder. This disorder occurs when the heart muscle does not get good blood flow. This condition will interfere with the function of the heart in flowing blood flow throughout the body. This study aims to develop a system capable of classifying people with heart disease using the Naïve Bayes method. Naïve Bayes is a method that works based on the probability that a person has a heart disease or not based on their medical record data. This algorithm is used with the aim of calculating the probability of a person suffering from heart disease based on their medical records. This data was obtained from the University of California Irvine Machine Learning website with a total of 303 datasets with 13 attributes. This research was conducted by dividing the data into 75% for training data and 25% for training data. The results of this study indicate that the Naïve Bayes algorithm used gives a fairly high accuracy value of 86.84%.
Pemanfaatan Augmented Reality Untuk Media Pembelajaran Alat Transportasi Bagi Anak Tunagrahita Sedang Dimas Dwi Kurniawan; Donny Avianto
Journal of Information System Research (JOSH) Vol 5 No 1 (2023): Oktober 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i1.4394

Abstract

A comparison between conventional teaching methods and modern technology reveals a significant difference in the effectiveness and efficiency of educating moderately mentally handicapped children or D3 C class. The available learning materials lack innovation. Monotonous material design that doesn't support interactivity can reduce the interest and engagement of mentally handicapped children. Therefore, the development of more engaging materials that meet their needs is required. D3 C class students with intellectual disabilities require special attention in the use of educational media, considering the current learning materials tend to be monotonous and lacking innovation. Therefore, Augmented Reality (AR)-based learning media can be a promising solution to enhance the quality of learning about various modes of transportation. Interactive AR-based applications enable the introduction of various modes of transportation in a more engaging and interactive way, involving visual and auditory aspects in learning. In application testing, it was proven that the speed of displaying 3D objects was very fast, taking only 2.3 seconds. This contributes to smooth and effective learning for D3 C class mentally handicapped children. Surveys conducted with teachers who have used this AR application indicate a tendency towards positive responses, with the majority of teachers responding "Agree" or "Strongly Agree" to the 10 statements provided. The use of AR in the MDLC approach offers significant potential for improving the education of D3 C class mentally handicapped children. Positive responses to AR reflect innovation in this learning approach, which focuses on individual development, integrated support, and inclusive education. However, it is important to always emphasize supervision to ensure effective AR use, so that children can benefit from a more engaging and tailored learning experience. Thus, the use of AR in the education of D3 C class mentally handicapped children has significant potential to provide innovative and effective learning in various modes of transportation, thereby helping them in their educational process. This research creates a learning method for studying means of transportation for moderately mentally handicapped children using AR technology, which provides innovation in modern times.
Penerapan Metode Naive Bayes pada Sistem Klasifikasi Kualitas Biji Kopi Robusta Muhammad Irsyad Indra Fata; 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.v5i1.515

Abstract

In Indonesia, coffee has become a necessity for some people because the effects can increase stamina and mood so that it can make a person's activities more productive, therefore quality coffee beans are needed so that coffee drinks can produce maximum effects and can be enjoyed by the public This is the basis for researchers to conduct research on the classification of superior quality coffee beans with accurate accuracy. Researchers use the naïve Bayes classifier method because this algorithm is considered more effective in cases of object classification compared to classification carried out manually. Researchers used 220 primary data with parameter configurations, namely, test size 0.2, random state 15, and Gausian model. The accuracy results obtained after testing were 0.91 for training data and 0.86 for testing data. It was concluded that the Naive Bayes method proved to be quite effective for classifying the coffee beans studied
Implementasi Algoritma LSTM untuk Prediksi Harga Cabai Merah Keriting di Yogyakarta Arieska Restu Harpian Dwika; 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.v5i1.534

Abstract

Curly red chili is one of the horticultural crops with high economic value. Curly red chili can cause inflation because the price fluctuates greatly and tends to rise. The purpose of the research is to predict future prices quickly and accurately so that the government and consumers can take preventive action against existing problems. This research will use the LSTM algorithm and a dataset of curly red chili prices in Yogyakarta from October 9, 2017 to October 27, 2023. Based on the testing of this research, the best results obtained are by using a data division ratio of 70%: 30%, epoch 150, batch size 48, learning rate 0.001, number of neurons 30, activation function ReLU, and the optimization function Adam which produces a MAPE value of 3.6995%, and an accuracy of 96.3005%. It is hoped that this system can help related parties get accurate curly red chili price predictions.
Perancangan Aplikasi Layanan Pengaduan Kerusakan Jalan Berbasis Android Rizky Samudra Falasyfa; 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.v5i1.569

Abstract

The android-based road damage complaint service application is a technological innovation that aims to improve the reporting and management of road damage. In the era of digitalization, people expect a lot from public services, especially infrastructure. However, the reporting process is still often done manually, causing weaknesses such as data loss, slow response and ineffectiveness. This application is an effective solution to overcome these problems. Users can easily report road damage through their Android devices. The benefit of this application is the ability to know the level of road damage in each report, making it easier for the authorities to determine the priority of handling. In addition, the app allows users to provide a detailed description of the damage, upload photos as evidence, and add additional relevant information. The app provides a complete and easy-to-fill form, making the reporting process more efficient.Once the report is submitted, users will receive a confirmation of report receipt to track the status of the report. The relevant agencies will utilize the app as a tool to manage incoming complaints, promptly responding to reports and processing them based on the priority and type of damage reported. Therefore, this application brings significant changes by improving public services and ensuring better infrastructure maintenance.
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