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All Journal Jurnal Pseudocode Journal of Information Systems Engineering and Business Intelligence Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Eksplora Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknoinfo Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Infomedia JURNAL PengaMAS MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Informatika dan Rekayasa Elektronik Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) Information System Journal (INFOS) JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer Jurnal Pengabdian Mitra Masyarakat (JPMM) International Journal of Advanced Science Computing and Engineering SmartComp Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen Scientific Journal of Informatics SWAGATI: Journal of Community Service Edu Komputika Journal Jurnal Pengabdian Masyarakat Inovasi Indonesia
Yoga Pristyanto
Universitas Amikom Yogyakarta
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Medicine & Pharmacology Public Health Social Sciences Other

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PEMANFAAT INVENTORY SYSTEM UNTUK MENINGKATKAN EFISIENSI DAN KINERJA PADA UMKM MANASTA FOOD Yoga Pristyanto; Anggit Ferdita Nugraha; Rohmad Fajarudin; Lucky Adhikrisna Wirasakti
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Publisher : UNIVERSITAS KHAIRUN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/pengamas.v5i2.3519

Abstract

Manasta Food merupakan salah satu usaha mikro kecil menengah yang bergerak dibidang penjualan bahan makanan. Manasta Food berlokasi Jalan Jatinom Penggung KM 1 Dusun Jetis, Kelurahan Padas, Kecamatan Karanganom, Kabupatan Klaten, Provinsi Jawa Tengah. Manasta Food didirikan oleh Bapak Sriyanta yang juga selaku pemilik kurang lebih sekitar awal tahun 2020. Pada proses bisnisnya Manasta Food bergerak dalam penjualan bahan makanan beku seperti bakso, olahan ikan, nugget, ayam, sosis dan beberapa produk beku lainnya. Seiring dengan berkembangnya Manasta Food, terdapat kendala dalam proses manajemen persediaan/inventarisasi barang. Selama ini proses manajemen persediaan/inventarisasi barang masih manual sehingga membutuhkan waktu yang cukup lama dalam pemrosesan barang dan transaksi. Oleh karena itu inventory system merupakan solusi untuk mengatasi permasalahan tersebut. Dengan adanya inventory system dapat meningkatkan kinerja dan efisiensi waktu dalam proses manajemen barang dan proses transaksi jual beli poduk. Selain itu sistem ini dapat digunakan juga untuk memantau trend transaksi barang sehingga pemilik Manasta bisa mengetahui barang apa saja yang sedang laris dalam periode waktu tertentu.
CLASSIFICATION OF CORN PLANT DISEASES USING VARIOUS CONVOLUTIONAL NEURAL NETWORK Aditya Yoga Pratama; Yoga Pristyanto
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 1 (2023): JITK Issue August 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i1.4258

Abstract

Based on data from the East Java Badan Pusat Statistik (BPS) in 2020, corn production in 2019 decreased by 622,403 tons. The decrease in production was caused by a disease that attacked corn plants identified from the corn leaves' physical appearance. This study aims to obtain an architectural model with good performance between AlexNet, LeNet, and MobileNet in detecting diseases of maize plants. The dataset used in this study came from Kaggle, with 4188 images divided into four disease classes: Common Rust, Gray Leaf Spot, Blight, and Healthy. Agricultural experts from Bantul have confirmed the appearance of each class of corn plant diseases. The preprocessing process is carried out to prepare the data so that the amount of data for each class is balanced. The image data used in this study totaled 4000 images which were divided into training data and testing data with a ratio of 80:20. Based on the experimental results, it was found that the MobileNet architecture has better performance than AlexNet and LeNet with an accuracy value of 83.37%, average precision of 0.8337, and g-mean of 0.8298. These results have been validated by agricultural experts in Bantul Regency and corn farmers experienced in corn farming.
DIAGNOSE OF MENTAL ILLNESS USING FORWARD CHAINING AND CERTAINTY FACTOR Marcheilla Trecya Anindita; Yoga Pristyanto; Heri Sismoro; Atik Nurmasani; Anggit Ferdita Nugraha
Jurnal Techno Nusa Mandiri Vol 20 No 2 (2023): Techno Nusa Mandiri : Journal of Computing and Information Technology Period of
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v20i2.4330

Abstract

The prevalence of mental disorders in Indonesia is increasingly significant, as seen from the 2018 Riskesdas data. Riskesdas records mental, emotional health problems (depression and anxiety) as much as 9.8%. This shows an increase when compared to the 2013 Riskesdas data of 6%. Based on these data, it can be said that many people still suffer from mental disorders. Meanwhile, the number of medical personnel, medicines and public treatment facilities for people with mental disorders is still limited. In addition, the lack of public awareness, concern and knowledge about mental health causes a lack of public interest in consulting a psychologist, so people tend to self-diagnose. One solution for self-diagnosis is to use an expert system. This study developed an expert system using the forward chaining method and certainty factor. Based on the research conducted, the results are as follows. First, the expert-based system that has been developed can help provide the results of a diagnosis that is carried out before there are complaints and will be detected early by efforts to increase awareness of the prevention of mental illness and reduce the tendency to self-diagnose. Second, applying the forward chaining method and certainty factor to this expert system can produce an accuracy rate of 95.918%. An expert has also validated these results; in this study, the expert was a psychologist at a hospital in Yogyakarta.
PERBANDINGAN KLASIFIKASI ALGORITMA K-NN, NEURAL NETWORK, NAÏVE BAYES, C 4.5 UNTUK MENDETEKSI WEB PHISING Eza Nanda; Istikomah; Nurindah A Amari; Yoga Pristyanto
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 16 No 3 (2018): September 2018
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v16i3.88

Abstract

The increasing number of internet users in the world and the rise of web phishing. Based on the Wearesocial report, there are several facts which include the number of world internet users that have reached 4.021 billion people. explained that of the hundreds of millions of internet users in Indonesia, 60 percent have accessed the internet using smartphones. Internet usage is dominated by socializing activities in cyberspace. Evidenced by the large number of world social media users, reaching 3,196 billion users. But with the rise of internet users and social media accompanied by the proliferation of phishing webs. The purpose of this attack is to make users believe that they interact with the official virtual or online site in question. Generally the information sought is in the form of a password, account or victim's credit card number. by way of being directed to a fake web or sending an email, banner or pop-up where the victim is asked to give his personal information. In this study C.4.5, Neural Network and K-Nerest Neighbor algorithm comparison will be conducted to determine the performance of the algorithm in detecting webphising by using classification techniques. Based on the model testing based on the Naïve Bayes method, Decision Tree C.4.5, K-NN, Neural Network uses the Weka framework v.3.8.2. The results of the Decision Tree C.4.5 algorithm have a higher level of accuracy with 89.66 percent accuracy.
Stock Price Time Series Data Forecasting Using the Light Gradient Boosting Machine (LightGBM) Model Anggit Dwi Hartanto; Yanuar Nur Kholik; Yoga Pristyanto
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.4.01740

Abstract

In the world of stock investment, one of the things that commonly happens is stock price fluctuations or the ups and downs of stock prices. As a result of these fluctuations, many novice investors are afraid to play stocks. However, on the other hand, stocks are a type of investment that can be relied upon during disasters or economic turmoil, such as in 2019, namely the Covid-19 pandemic. For stock price fluctuations to be estimated by investors, it is necessary to carry out a forecasting activity. This study builds stock price forecasting using the Light Gradient Boosting Machine (LightGBM) algorithm, which has high accuracy and efficiency. To forecast stock price time series, the model used is the LightGBM ensemble. At the same time, they were optimizing the determination of hyperparameters using Grid Search Cross Validation (GSCV). This study will also compare the LGBM algorithm with other algorithms to see which model is optimal in forecasting price stock data. In this study, the test used the RMSE metric by comparing the original data (testing data) with the predicted results. The experimental results show that the LightGBM model can compete with and outperform boosting-based forecasting models like XGBoost, AdaBoost, and CatBoost. In comparing forecasting models, the same dataset is used so that the results are accurate, and the comparisons are equivalent. In future research, paying attention to the data during pre-processing is necessary because it has many outliers. In addition, it is necessary to include exogenous variables and external variables, which are determined to involve many parties.
DIAGNOSIS OF CUCUMBER PLANT DISEASES USING CERTAINTY FACTOR AND FORWARD CHAINING METHODS Bligania Bligania; Yoga Pristyanto; Heri Sismoro; Yuli Astuti; Anggit Ferdita Nugraha
Jurnal Techno Nusa Mandiri Vol 21 No 1 (2024): Techno Nusa Mandiri : Journal of Computing and Information Technology Period of
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v21i1.5355

Abstract

Cucumber plants spread and can live in tropical climates like Indonesia. The cucumber plant has many benefits and can be a beauty ingredient. Cucumbers, like other plants, can also have disease attacks, which can threaten farmers. This expert system can help farmers discover diseases that attack cucumber plants and how to control them. The certainty Factor is a method used to measure the certainty of facts to describe an expert's confidence in facing a problem. Forward Chaining is an approach method monitored by data starting from information in the form of facts and supported by rules to reach conclusions. Implementing an expert system for diagnosing cucumber diseases using certainty factor and forward chaining methods will make it easier for farmers and the public to cultivate cucumber plants and get good results. Applying the forward chaining method and factor certainty in this expert system can produce an accuracy level of 95.918%.
Model Balanced Bagging Berbasis Decision Tree Pada Dataset Imbalanced Class Pristyanto, Yoga; Zein, Aditya Ahmad
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.v12i1.1399

Abstract

Algoritma klasifikasi merupakan algoritma yang sangat sering digunakan beriringan dengan kebutuhan manusia, namun peneliti an sebelumnya sering dijumpai kendala saat menggunakan algoritma klasifikasi. Salah satu permasalahan yang sering sekali dijumpai ialah kasus imbalanced dataset. Sehingga dalam penelitian ini diusulkan ensemble method untuk mengatasinya, salah satu algoritma ensemble method yang terkenal ialah bagging. Implementasi balanced-bagging digunakan untuk meningkatkan kemampuan dari algoritma bagging. Dalam penelitian ini melibatkan perbandingan tiga model klasifikasi berbeda dengan lima dataset yang memiliki imbalanced ratio (IR) yang berbeda, Model akan dievaluasi berdasarkan metrik akurasi (balanced accuracy), geometric mean dan area under curve (AUC). Model pertama merupakan proses klasifikasi menggunakan Decision Tree (tanpa Bagging),  Model kedua merupakan proses klasifikasi menggunakan Decision Tree (dengan Bagging) dan model ketiga menggunakan Decision Tree (dengan Balanced-Bagging). Implementasi metode bagging dan balanced bagging terhadap algoritma klasifikasi Decision Tree mampu meningkatkan kinerja hasil akurasi (balanced accuracy), geometric mean, dan AUC. Secara umum model Decision Tree + Balanced Bagging menghasilkan kinerja yang terbaik pada seluruh dataset yang digunakan.
Sistem Pendukung Keputusan Penilaian Kinerja Guru Di SMK Muhammadiyah Imogiri Menggunakan Metode Profile Matching Rospita, Andri; Pristyanto, Yoga; Dahlan, Akhmad
Jurnal Eksplora Informatika Vol 12 No 1 (2022): Jurnal Eksplora Informatika
Publisher : Institut Teknologi dan Bisnis STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/eksplora.v12i1.613

Abstract

Guru mempunyai andil yang besar terhadap keberhasilan pembelajaran di sekolah, sehingga penting untuk dilakukan penilaian kinerja guru untuk mengetahui sejauh mana kemampuan guru dalam menjalankan tugasnya. Saat ini penilaian kinerja guru di SMK Muhammadiyah Imogiri masih dilakukan secara manual sehingga sering terjadi kesalahan dalam melakukan perhitungan nilai dan memerlukan waktu yang lama untuk mengelola data tersebut. Berdasarkan permasalahan yang ada maka dirancanglah sistem pendukung keputusan dengan menerapkan metode profile matching. Dengan adanya sistem ini penilaian kinerja guru dapat lebih efektif dan efisien. Proses pada metode profile matching adalah dengan membandingkan antar kompetensi individu ke dalam potensi suatu jabatan sehingga dapat diketahui perbedaan kompetensinya (GAP). Metode tersebut memiliki tingkat objektifitas yang lebih baik dibanding metode lain dan mempertimbangkan konsistensi yang logis dalam penilaian. Hasil pengujian didapatkan akurasi sistem 93,33% terhadap 30 data yang diuji, hal ini menunjukkan bahwa penerapan metode profile pada sistem mempunyai tingkat akurasi yang baik dan dapat mengatasi permasalahan yang ada.
Implementation of the Simple Additive Weighting Method in Determining for Village Fund Assistance Recipients Putra, Frahma Aditya; Fauzy, Marwan Noor; Pristyanto, Yoga; Hidayat, Kardilah Rohmat
Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer Vol 4 No 1 (2024): JTECS Januari 2024
Publisher : FAKULTAS TEKNIK UNIVERSITAS ISLAM KADIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32503/jtecs.v4i1.4862

Abstract

Bantuan Langsung Tunai Dana Desa (BLT DD) merupakan program dari pemerintah dengan memberikan bantuan langsung tunai kepada masyarakat yang berasal dari Dana Desa dengan tujuan untuk meningkatkan kualitas hidup masyarakat desa dan meningkatkan kesejahteraan sosial. Namun, sistem yang berlaku saat ini kemungkinan besar akan menimbulkan subjektivitas tertentu dalam pemilu karena kebebasan mengambil keputusan tidak memiliki dasar yang jelas. Selain itu, sistem pemilu masih mempunyai banyak keterbatasan akibat otomatisasi sistem sehingga menimbulkan beban kerja yang tidak sesuai dengan hasil yang dicapai. Oleh karena itu, diperlukan suatu sistem yang dapat menjadi alat atau alternatif dalam pengambilan keputusan untuk mengidentifikasi pihak-pihak yang dapat memperoleh manfaat dari dukungan keuangan desa. Maka dari permasalahan tersebut peneliti berkeinginan untuk membuat suatu sistem pendukung keputusan dengan mengimplementasikan metode Simple Additive Weighting (SAW) pada komputer berharap dapat menjadi solusi yang cocok. Hasil yang diperoleh dari penelitian ini menunjukkan bahwa metode Simple Additive Weighting (SAW) telah berhasil diterapkan dalam sistem pendukung Keputusan untuk mengidentifikasi calon penerima bantuan langsung tunai dari Dana Desa
Model Random Forest and Support Vector Machine for Flood Classification in Indonesia Purwati, Sintia Eka; Yoga Pristyanto
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13973

Abstract

People, especially those living in lowland areas and along rivers. This flood phenomenon significantly affects various aspects, both in terms of economics, environment, and public safety. Flooding is a disaster that often causes problems for most people, especially those living in lowland areas and on riverbanks. This flood phenomenon significantly affects various aspects, such as the economy, environment, and community safety. This research compares the Random Forest and Support Vector Machine (SVM) methods for flood classification in Jakarta. The data used is flood data from 2016 – 2020 in Jakarta, obtained from Kaggle. Model performance evaluation is carried out using accuracy, precision, recall, and F1- Score metrics. The analysis results show that both models accurately classification floods, with Random Forest showing a more stable performance than SVM.
Co-Authors Acihmah Sidauruk Aditya Yoga Pratama Afrig Aminuddin Aisha Shakila Iedwan Akhmad Dahlan Alvin Rahman Al Musyaffa Andi Sunyoto Anggi Thoat Ariyanto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto, Anggit Dwi Anggita, Sharazita Dyah Anna Baita arif nur rohman Arif Nur Rohman Asti Astuti, Ika Atik Nurmasani ATIK NURMASANI Atik Nurmasani Barus, Herianta Bety Wulan Sari Bety Wulan Sari, Bety Wulan Bligania Bligania Cherfly Kaope Donni Prabowo, Donni Dwi Hartanto, Anggit Dyah Anggita, Sharazita Eli Pujastuti, Eli Eza Nanda Fadhilah Dwi Ananda Fajri, Ika Nur Fauzy, Marwan Noor Gagah Gumelar Gita Cahyani Hendra Kurniawan Heri Sismoro Hidayat, Kardilah Rohmat Ibnu Hadi Purwanto Ibrahim Aji Fajar Romadhon Iedwan, Aisha Shakila Ike Verawati Ikmah Ikmah Irfan Pratama Istikomah Khoiruddin, Lukman Kono, Maria Fatima Kristianti, Fanny Novatriana Lucky Adhikrisna Wirasakti Mambaul Hisam Marcheilla Trecya Anindita Maulana, Ariefhan Mauliza, Nia Mukarabiman, Zulfikar Mulia Sulistiyono Nia Mauliza Nia Mauliza Nugraha, Anggit Ferdita Nuri Cahyono Nurindah A Amari Purwati, Sintia Eka Putra, Frahma Aditya Rahman Saputra, Rahman Rifda Faticha Alfa Aziza Rizky Hafizh Jatmiko Rohmad Fajarudin Rohman, Arif Nur Romadhon, Ibrahim Aji Fajar Rospita, Andri Sabella, Cindy Dinda Sifa’ul Husna, Siti Okta Sumarni Adi Windarni, Vikky Aprelia Wirantanu, Dipa Wirasakti, Lucky Adhikrisna Wiwi Widayani Wulandari, Irma Rofni Yanuar Nur Kholik Yudiyanto, Muhammad Resa Arif Yuli Astuti Zein, Aditya Ahmad