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All Journal Jurnal sistem informasi, Teknologi informasi dan komputer JURNAL MEDIA INFORMATIKA BUDIDARMA KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Building of Informatics, Technology and Science TIN: TERAPAN INFORMATIKA NUSANTARA Brahmana : Jurnal Penerapan Kecerdasan Buatan Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI JPM: JURNAL PENGABDIAN MASYARAKAT Bulletin of Computer Science Research Jurnal Janitra Informatika dan Sistem Informasi BEES: Bulletin of Electrical and Electronics Engineering Bulletin of Data Science JOMLAI: Journal of Machine Learning and Artificial Intelligence Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Winanjaya, Riki
STIKOM Tunas Bangsa Pematangsiantar, Indonesia
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Implementasi Data Mining Menggunakan Algoritma Apriori Dalam Menentukan Persediaan Barang : Studi Kasus: Toko Sinar Harahap Putri Mai Sarah Tarigan; Jaya Tata Hardinata; Hendry Qurniawan; M Safii; Riki Winanjaya
Jurnal Janitra Informatika dan Sistem Informasi Vol. 2 No. 1 (2022): April - Jurnal Janitra Informatika dan Sistem Informasi
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25008/janitra.v2i1.142

Abstract

UMKM ialah kegiatan usaha kecil ekonomi rakyat yang berskala kecil dan dilindungi dari kompetisi usaha yang tak sehat dan tak setara. Wirausaha yang bergerak dibidang pertokoan memiliki prospek yang menjanjikan, karena dapat melayanin masyarakat dengan kategori ekonomi menengah kebawah dan ke atas serta bisa mempermudah masyarakat untuk berbelanja keperluan tiap hari tanpa harus belanja ke supermarket atau swalayan. Namun persediaan barang atau bahan kebutuhan yang tidak dilakukan secara optimal dapat menyebabkan kekosongan pada barang atau bahan kebutuhan tersebut. Hal tersebut juga terjadi pada toko sinar harahap yang sering mengalami kekosongan pada persediaan beberapa barang dan kebutuhan yang di cari oleh pelanggan, ini di akibatkan dari tidak adanya kebiasaan pengontrolan persediaan pada toko. Maka penelitian ini bertujuan untuk melihat barang dan kebutuhan apa saja yang dibutuhkan oleh pelanggan toko. Penelitian ini menggunakan beberapa variabel yaitu tanggal transaksi, nama produk serta jumlah penjualan/pembelian. Maka, dari hasil penelitian menggunakan algoritma apriori tersebut akan di dapat data nama barang yang paling banyak terjual untuk di jadikan sebagai antisipasi persediaan barang agar tidak mengalami kekosongan yang dapat menyebabkan pelanggan kecewa.
Implementation of the Weighted Moving Average Method for Forecasting the Production of Manila Duck meat in Indonesia Diana Pratiwi; Riki Winanjaya; Irawan Irawan
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 3 (2022): September
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (723.134 KB) | DOI: 10.55123/jomlai.v1i3.916

Abstract

Manila duck is a waterfowl originating from South America, through the Philippines this type of duck entered Indonesia and has a large distribution in various regions of Indonesia the production on manila duck meat and from 2019-2020 has decreased due to the covid-19 pandemic which resulted in economic difficulties. And the lack of demand from restaurants and households so that the amount of production decrease. However, in 2020-2021 production will increase due to the relaxation from the previous pandemic and the demand and marketing has increased to that the number of production has increased from the previous year. The Weighted Moving Average method is a method used to determine the latest trend with a moving average value. The purpose of this study was to analyses the amount of production of manila duck meat in solving the problem. The result obtained with the smallest error percentage are at F128 in the province of North Maluku with MAPE value of 0,003 or equal to 0,3% with a bias of -0,25, MAD 0,25, MSE 0,06, with a forecasting value of 83,29 which is close to the original data, namely 83,04 so that the forecast value for 2022 is 83,24 tons.
Forecasting of Rubber Production in North Sumatra with Backpropagation Algorithm Josua Fernando Simanjuntak; Riki Winanjaya; Wendi Robiansyah
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 3 (2022): September
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (693.975 KB) | DOI: 10.55123/jomlai.v1i3.917

Abstract

Rubber is a commodity to produce tires, balloons, and other rubber-based products. Indonesia is the second largest rubber producer and distributor in the world. But, rubber production level tends to fluctuate. Therefore, an analysis is needed to predict rubber production in the future thus rubber plantations, especially folk-owned, can take steps to prevent if declines in production are found. One way that can be done to predict is by utilizing Artificial Neural Network with Backpropagation method, since it provides accurate results. In this research, 10 network architecture models were tested and the best architecture achieved was 10-10-11-1 with accuracy of 96%. With that architecture, predictions are done and resulted in estimated rubber production in North Sumatra for 2021-2025.
Artificial Neural Network Method in Predicting the Amount of Manila Duck Meat Production by Province in Indonesia Joko Pamungkas; Riki Winanjaya; Wendi Robiansyah
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 3 (2022): September
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (646.291 KB) | DOI: 10.55123/jomlai.v1i3.918

Abstract

Duck meat is a source of animal protein that many Indonesians need because it can increase nutritional needs to improve people's quality of life. One of the types of ducks used in this study is the Manila duck, this type of duck was chosen because it is very easy to maintain and the price is also relatively affordable. Based on data on the production of Manila ducks in Indonesia from several provinces, the annual production amount is unstable. Therefore, it is important to make predictions about this matter as information for the government. The data sample used in this study is manila duck production data taken from the Indonesian Central Statistics Agency in 2017-2020. This research uses backpropagation algorithm. Based on the results of the analysis, the best architectural model is 3-6-1 which will later be used to predict the amount of manila duck meat production in 2022 because it has the highest accuracy rate compared to other models, which is 74%. MSE Testing is 0,00412. Based on this model, predictions of the amount of manila duck meat production will be made based on provinces in Indonesia. From the prediction results, it can be seen that there are 25 provinces that are estimated to experience an increase in production in 2022 or around 73,5% (25 provinces) of a total of 34 provinces in Indonesia. Meanwhile, 9 other provinces experienced a decline or around 26,5%.
IMPLEMENTASI DATA MINING MENGGUNAKAN ALGORITMA APRIORI DALAM MENENTUKAN PERSEDIAAN BARANG (STUDI KASUS : TOKO SINAR HARAHAP) Putri Mai Sarah Tarigan; Jaya Tata Hardinata; Hendry Qurniawan; Muhammad Safii; Riki Winanjaya
JUST IT : Jurnal Sistem Informasi, Teknologi Informasi dan Komputer Volume 12 No 2 Tahun 2022
Publisher : Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24853/justit.12.2.%p

Abstract

UMKM ialah kegiatan usaha kecil ekonomi rakyat yang berskala kecil dan dilindungi dari kompetisi usaha yang tak sehat dan tak setara. Wirausaha yang bergerak dibidang pertokoan memiliki prospek yang menjanjikan, karena dapat melayanin masyarakat dengan kategori ekonomi menengah kebawah dan ke atas serta bisa mempermudah masyarakat untuk berbelanja keperluan tiap hari tanpa harus belanja ke supermarket atau swalayan. Namun persediaan barang atau bahan kebutuhan yang tidak dilakukan secara optimal dapat menyebabkan kekosongan pada barang atau bahan kebutuhan tersebut. Hal tersebut juga terjadi pada toko sinar harahap yang sering mengalami kekosongan pada persediaan beberapa barang dan kebutuhan yang di cari oleh pelanggan, ini di akibatkan dari tidak adanya kebiasaan pengontrolan persediaan pada toko. Maka penelitian ini bertujuan untuk melihat barang dan kebutuhan apa saja yang dibutuhkan oleh pelanggan toko. Penelitian ini menggunakan beberapa variabel yaitu tanggal transaksi, nama produk serta jumlah penjualan/pembelian. Maka, dari hasil penelitian menggunakan algoritma apriori tersebut akan di dapat data nama barang yang paling banyak terjual untuk di jadikan sebagai antisipasi persediaan barang agar tidak mengalami kekosongan yang dapat menyebabkan pelanggan kecewaKata Kunci: persediaan, barang, penjualan, data mining, algoritma apriori
Analisis Pengaruh Komposisi Data Training dan Testing Terhadap Akurasi Algoritma Resilient Backpropagation (RProp) Harly Okprana; Riki Winanjaya
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 4, No 1 (2022): Edisi Desember
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v4i1.138

Abstract

Prediction classification accuracy is a measure of success and satisfaction in predicting past data to produce accurate predictions, knowing how precise a classification pattern predicts class data from future data. In practice, artificial neural networks test the accuracy of a classification pattern using data testing, while to find the pattern itself, use training data. Errors in determining the composition of the presentation of training and testing data can affect the accuracy value obtained, therefore the distribution of the presentation of the amount of data from a dataset is one of the determining factors for the amount of accuracy. This study uses a dataset of Michigan Computer English Course students in 2018-2019 using the Resilient Backpropagation (RProp) method. The data processed was 100 student data for 2018-2019. By dividing the composition of 25% training data with 75% data testing with an accuracy value of 99.25% while dividing 50% training data with 50% data testing with an accuracy value of 100% as well as dividing 75% training data with 25% data testing with a value 100% accuracy.
Optimalisasi JST dalam Memprediksi Kunjungan Wisatawan Mancanegara Untuk Perencanaan dan Pengembangan Pariwisata yang Efektif Riki Winanjaya; Harly Okprana
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.v7i4.6739

Abstract

Foreign tourist predictions assist the government and stakeholders in planning long- and short-term tourism strategies. Accurate information on the estimated number of tourists enables appropriate infrastructure development, efficient budget allocation and setting of relevant policies. Foreign tourists discussed in this study are foreign tourists based in ASEAN countries. This research will utilize historical data on foreign tourist arrivals from the Ministry of Tourism, the Ministry of Law and Human Rights (Directorate General of Immigration) and Mobile Positioning Data. The data that has been obtained will be processed and filtered to obtain relevant and accurate data before being used as input in the creation of an Artificial Neural Network (ANN) model. The algorithm proposed in this study is the Cyclical Rule algorithm with the optimization of the Bayesian Regulation algorithm, which can be used to solve data prediction problems. This study was analyzed using 10 (ten) architectural models, including 4-4-1, 4-5-1, 4-8-1, 4-10-1, 4-12-1, 4-15-1, 4-16-1, 4-20-1, 4-24-1, and 4-25-1. Based on the analysis, the results obtained from the 4-10-1 model with the optimization of the Bayesian Regulation algorithm as the best model with the smallest testing MSE compared to the other models, equal to 0.00786961. Based on the prediction results, foreign tourist arrivals from ASEAN countries in 2023 are expected to decrease compared to 2022. Tourism actors can take advantage of the results of this prediction to improve the quality and quantity of services provided to tourists, as well as adjust the needs of tourists with the resources available at tourist destinations.
Pelatihan Pembuatan Pin Press Digital Bagi Siswa untuk Meningkatkan Keterampilan dan Menumbuhkan Semangat Wirausaha Achmad Daengs GS; Rizky Khairunnisa Sormin; Zulia Almaida Siregar; Riki Winanjaya; Anjar Wanto
JPM: Jurnal Pengabdian Masyarakat Vol. 4 No. 2 (2023): October 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jpm.v4i2.1326

Abstract

This PKM activity aims to develop the skills of Vocational High School (SMK) students in making digital pin presses through special training. Technology and mechanical skills are becoming increasingly important in today's digital era, and vocational schools have a key role in preparing students for the ever-changing world of work. The training was carried out at the Anak Bangsa Private Vocational School located in Simalungun Regency. In this activity, we designed and implemented intensive training for vocational school students on making digital pin presses. This training includes a basic understanding of digital technology, use of relevant hardware and software, as well as technical skills required in the manufacturing process. Apart from that, the team leader also provided material about the entrepreneurial spirit which was carried out using the Zoom application. The training method involves practical teaching and guidance from experienced instructors. The results of the activity show that this training was successful in improving students' skills in making digital pin presses. In addition, students also demonstrated improvements in their understanding of digital technology and its potential use in the world of work. It is hoped that this activity can make a positive contribution to developing the skills of vocational school students and help them prepare to enter an increasingly competitive job market. The results of this training can be used as a basis for developing similar training in other vocational schools and can be a reference for educational institutions and the government in efforts to improve the quality of vocational education in Indonesia.
Sistem Pendukung Keputusan Pemilihan Rumah Sakit Terbaik Di Kota Pematangsiantar Dengan Menggunakan Metode TOPSIS Sanri Sunervia Yunika Damanik; S Saifullah; Riki Winanjaya
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 2, No 2 (2021): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v2i2.62

Abstract

A hospital is a place where health services are provided by doctors, nurses and other health professionals. Health is the most important thing that every human wants to survive in doing all activities. The importance of this health encourages the government and the private sector to build quality hospitals so that people can access health needs. However, it is not only quality that is desired by the community, but satisfaction in providing fast services, supporting facilities and hygiene and safety are needed by the community so that the healing process feels happy and safe. To find out which hospital has the provision of health services desired by the community, a decision support system is needed. Decision support system is a system that can be used as a tool assist in the selection of hospitals that are in charge based on criteria desired by the community. The method used by researchers is the Topsis method. This method was chosen because it is able to select alternatives from several alternatives based on predetermined criteria. The results of this study are an average value of each alternative and criteria that will be ranked as the best hospital in Pematangsiantar. Based on the results of these tests can be a foundation that can help the community in choosing the provision of hospital services for the healing process.
Penerapan Metode K-Means Dalam Mengelompokkan Banyaknya Desa/Kelurahan Menurut Jenis Pencemaran Lingkungan Hidup Berdasarkan Provinsi Agus Tiranda Sipayung; S Saifullah; Riki Winanjaya
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 1, No 4 (2020): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v1i4.35

Abstract

Environmental pollution is hazardous for every living thing; environmental pollution can cause an imbalance in the environment or existing ecosystem. This study discusses the grouping of villages according to the type of environmental pollution based on the provinces in Indonesia. The method used is DataMining with the K-means Clustering algorithm. By using this method, the data obtained can be grouped into 2 clusters. This study uses secondary data, namely data obtained through intermediary media recorded on the Central Bureau of Statistics website with the URL address: http://www.bps.go.id. The results obtained in this study are grouping environmental pollution into 2 clusters, namely the highest cluster and the lowest cluster. In this research, it is hoped that it can provide input to related government parties to pay more attention to the provinces included in the highest cluster to tackle environmental pollution in the province.
Co-Authors Agus Perdana Windarto Agus Tiranda Sipayung Diana Pratiwi Elischa Febrivani Fadillah Alwi Pambudi Fitri Anggraini, Fitri Fitri Rizki GS , Achmad Daengs GS, Acmad Daengs Harliana Harliana Hartama, Dedy Hendry Qurniawan Hendry Qurniawan Indra Riyana Rahadjeng Irawan Irawan Irfan Sudahri Damanik Jaya Tata Hardinata Joko Pamungkas Josua Fernando Simanjuntak M Safii Mhd Wendico Herdian Muhammad Fauzan Muhammad Safii Nugroho, Muhammad Rizky Tri Nurhayati Nurhayati Okprana, Harly Putri Mai Sarah Tarigan Putri Mai Sarah Tarigan Qurniawan, Hendry Rajes Wasimson Sinaga Reza Fauzy Rizki, Fitri Rizky Khairunnisa Sormin S Saifullah S Susianti Safii, M Saifullah Saifullah Saifullah Sanri Sunervia Yunika Damanik Sari, Hanifah Urbach Simanjorang, Fernandi Susiani Susiani Susiani Susiani, Susiani Syahputra, Muhammad Syahputra, Muhammad Riza Tarigan, Putri Mai Sarah Wanto, Anjar Wendi Robiansyah Yuni Radana Sembiring Yusuf Maulana Zulia Almaida Siregar