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Journal of Computing and Informatics Research
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 2808375X     DOI : -
Core Subject : Science,
Computer Science & IT
Fokus kajian Journal of Computing and Informatics Research mempublikasikan hasil-hasil penelitian pada bidang informatika, namun tidak terbatas pada bidang ilmu komputer yang lain, seperti: 1. Kriptografi, 2. Artificial Intelligence, 3. Expert System, 4. Decision Support System, 5. Data Mining, dan lainnya.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 88 Documents
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Implementasi Data Mining Pada Pola Penjualan Ulos Dengan Menggunakan Metode Apriori Femmy Fitria
Journal of Computing and Informatics Research Vol 2 No 3 (2023): July 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v2i3.847

Abstract

Seiring dengan berkembangnya teknologi informasi yang semakin hari semakin maju, membuat semakin berkembang pula kemampuan dalam menyelesaikan masalah yang ada. Dalam pola penjualan pada Graha Ulos Songket Medan masih banyak kendala yang sering terjadi. Salah satunya yaitu dalam pengumpulan data transaksi pola penjualan masih tergolong manual sehingga mengerjakannya membutuhkan waktu yang lama. Untuk mengatasi masalah yang terjadi, diperlukan data mining dengan metode Algoritma Apriori untuk pola penjualan ulos tersebut. Dengan hasil penelitian tersebut di harapkan dapat memudahkan karyawan untuk melakukan pengumpulan data transaksi pola penjualan pada Graha Ulos Songket Medan. Pada studi ini dihitung melalui tiga tahap iterasi pembentukan kandidat k-itemset. Aturan yang terbentuk dapat digunakan pengelola bisnis Graha Ulos Songket Medan untuk pengambilan keputusan dalam usaha meningkatkan nilai penjualan.
Analisis Metode Backpropagation Dalam Memprediksi Jumlah Produksi Daging Kambing Di Indonesia Rika Setiana; Siregar, Razalfa Aindi; Fahry Husaini; Agus Perdana Windarto
Journal of Computing and Informatics Research Vol 2 No 3 (2023): July 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v2i3.854

Abstract

A science that has always developed rapidly until now is artificial neural networks. A computational science that works like the human nervous system is an artificial neural network. Artificial neural networks with the backpropagation method can make a prediction on data. In this article, a prediction will be made on the amount of goat production in Indonesia. Goats are one of the livestock that can produce nutritious meat. The lack of goat meat will cause the price of goat meat to rise. Producing enough goat meat helps stabilize the price of meat, but if goat meat production is less than demand, it will lead to price increases. Therefore, looking at the problems above, this study aims to predict goat meat so that in the future it can know how much goat meat must be predicted by processing data first and then being used as input in predicting the amount of goat meat production. Prediction is one way to estimate future demand. Avoiding the lack of meat availability, by predicting the amount of goat meat produced in such a way that there is no scarcity of goat meat and fluctuations in the price of goat meat in the market. Basic methods and data are required to make predictions. In this study, data was obtained from BPS Indonesia in the livestock section using data from 2001-2021 as training data and 2002-2022 as test data. The method applied in this article is the backpropagation algorithm. This article applies 5 network architectures implemented in the mathlab application. The architecture used in this article is 20-25-1 with a Mean Squred Error testing 0.00447765, in 20-30-1 architecture produces Mean Squred Error 0.00300466, in 20-35-1 architecture produces 0.00426823, in 20-37-1 architecture produces 0.00357757. Based on the best architecture produced in this study, the 20-15-1 architecture with 90% accuracy with a Mean Squared Error testing 0.00262384 at epoch 27915 Iterations. Thus it can be concluded that the backpropagation algorithm can provide good accuracy in the prediction process. With this research, the livestock industry can utilize it as one of the materials to predict goat meat in the future
Analisa Metode Backpropagation Pada Prediksi Rata-rata Harga Beras Bulanan Di Tingkat Penggilingan Menurut Kualitas Dwira Azi Pragana; Manurung, Dicky Wahyudi; Agus Perdana Windarto
Journal of Computing and Informatics Research Vol 2 No 3 (2023): July 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v2i3.855

Abstract

Rice is a staple food in Indonesia and plays a crucial role in the food structure as a source of nutrition. The diverse population of Indonesia, spread across various islands, makes rice availability highly important. The government continues to strive for food security, particularly by increasing domestic production. These considerations become even more significant for Indonesia due to its growing population and extensive geographical distribution. To meet the population's food needs, Indonesia needs sufficient food supply and distribution to fulfill consumption and maintain adequate reserves for extensive logistical operations. Rice shortage can be seen as a threat to economic and political stability. The significance of rice as a food commodity means that it is constantly in demand by people from all walks of life. Price fluctuations over time due to imbalances between supply and demand have a significant impact on the middle class and working class. The instability of rice prices greatly affects both the general public and farmers. Generally, prices are determined by the interaction of supply and demand. If supply is high and demand is low, prices will decrease. Conversely, if supply is low and demand is high, prices will increase. Prediction is an important tool to anticipate future events by recognizing patterns from the past. Backpropagation can be used as a method to predict rice prices. The data used in this study are the average monthly rice prices at the milling level according to the quality of large-scale traders from January 2023 to December 2023, in Indonesian Rupiah per kilogram. This research utilizes data obtained from the website of the Indonesian Central Bureau of Statistics (BPS) from 2013 to 2022. The study employed 5 different architectures for data testing, namely the 15-15-1 architecture with a testing mean square error (MSE) of 0.00644604, the 15-19-1 architecture with a testing MSE of 0.01005532, the 15-30-1 architecture with a testing MSE of 0.02119922, the 15-31-1 architecture with a testing MSE of 0.00009938. The best architecture in this study was the 15-17-1 model with 5206 iterations and a runtime of 18 seconds, achieving the smallest testing MSE of 0.00000105 and the highest accuracy of 100%. From the obtained architectures, it is evident that backpropagation can perform with a high level of precision. This research can serve as a guideline for the government to determine rice availability and establish average rice prices based on quality, thus preventing future rice shortages.
Sistem Pengambil Keputusan Pemilihan Ketua Kelompok Tani Dengan Menggunakan Metode Profile Matching Haridison Siagian
Journal of Computing and Informatics Research Vol 2 No 3 (2023): July 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v2i3.932

Abstract

Ketika berbicara tentang kelompok tani, pemilihan seorang ketua yang efektif adalah faktor kunci dalam mencapai keberhasilan kelompok tersebut. Namun, sering kali, proses pemilihan ketua kelompok tani cenderung tidak efektif dan penuh dengan ketidakcocokan. Permasalahan ini muncul karena ketua kelompok tani seringkali gagal memberikan keputusan yang tegas ketika kelompok menghadapi masalah yang memerlukan penyelesaian cepat. Perbedaan pendapat seringkali muncul dalam proses pemilihan ketua kelompok tani, terutama karena adanya unsur kekeluargaan di dalam kelompok tersebut. Akibatnya, pemilihan ketua kelompok tani menjadi kurang efektif dan kurang optimal, menghambat kemajuan kelompok tani itu sendiri. Penelitian ini bertujuan untuk mengatasi masalah ini dengan menerapkan Metode Profile Matching dalam proses pemilihan ketua kelompok tani. Metode ini akan membantu mengevaluasi profil setiap calon ketua berdasarkan kriteria yang telah ditetapkan, yang akan mengurangi subjektivitas dalam proses pemilihan. Hasil penelitian ini diharapkan akan memberikan panduan yang lebih efektif dan objektif dalam pemilihan ketua kelompok tani, yang pada gilirannya akan meningkatkan kualitas kepemimpinan dan pengambilan keputusan di dalam kelompok tani. Dengan demikian, penelitian ini dapat menjadi landasan penting dalam meningkatkan produktivitas dan stabilitas kelompok tani serta mengatasi konflik yang sering terjadi dalam proses pemilihan ketua.
Optimasi Penerimaan Siswa Baru dengan Penerapan Algortima Text Mining dan TF-IDF Y Ayu Putri Gabriella S; Guidio L Ginting; Melda Panjaitan
Journal of Computing and Informatics Research Vol 2 No 3 (2023): July 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v2i3.941

Abstract

Sekolah Menengah Pertama Yayasan Perguruan merupakan salah satu sekolah yang banyak diminati oleh beberapa kalangan, dikarenakan jurusan yang bermacam-macam yang ada pada sekolah tersebut. Pada saat ini sekolah tersebut masih terbilang banyak memiliki permasalahan dalam penerimaan siswa baru nya. Banyak data yangmasuk tidak sesuai dengan apa yang telah diterima, dengan demikian sekolah perlu menerapkanperancangan aplikasi dalam penerimaan siswa baru agar dokumen dan data yang diterima lebih konkret, tidak hanya itu dengan menerapkan metode tersebut, pengerjaan dan proses pemasukan data dapat memberikan hasil yang maksimal. Metode yang digunakan dalam penelitian ini yaitu text mining dan TF- IDF,text mining sendiri yaitu metode yang memiliki tahapan untuk menemukan data menjadi lebih efektif dan akurat. Selain itu metode TF-IDF pun memiliki cara pengerjaan yang dapat dikatakan efisien dengan cara menghitung bobot pada setiap kata. Sistem penerimaan siswa baru ini dibangun menggunakan software dreamweaver, dan bahasa pemograman HTML, CSS, dan JavaScript serta menggunakan database MySQL sebagai database server. Hasil dari penelitian menggunakan metode ini dapat memberikan kemudahan pihak sekolah dan calon siswa dalam penerimaan siswa baru yang dilakukan secara online maupun offline, sehingga pendataan, dan penerimaan siswa baru dapat dilakukan dengan sistem otomatis yang tidak perlu menggunakan cara kerja yang bertele-tele.
Combination of Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) and Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA) in Determining the Best Cashier Sintaro, Sanriomi; Aldino, Ahmad Ari; Setiawansyah, Setiawansyah; Saputra, Very Hendra
Journal of Computing and Informatics Research Vol 3 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v3i1.969

Abstract

MOORA (Multi-Objective Optimization by Ratio Analysis) method is one of the multi-criteria analysis techniques used for alternative selection based on several different criteria or objectives. In the context of selecting the best cashier, by using the MOORA method to select cashiers based on several relevant criteria. While Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA) is a method used to assess the importance of criteria relative to each other in the context of multi-criteria analysis. This method helps in determining the weight of criteria used in multi-criteria decision making. The combination of MOORA and PIPRECIA will produce the best cashier selection based on the criteria used. The results of the best cashier assessment ranking using the Multi-Objective Optimization method on the basis of Ratio Analysis (MOORA) and the Simplified Pivot Pairwise Relative Criteria Importance Assessment weighting method obtained results, namely for Rank 1 obtained by Rini Maya with a final value of 0.343.
Decision Support System for Determining the Best Coffee Shop Applying the OCRA Method using ROC Weighting Erlin Windia Ambarsari; Hetty Rohayani; Ade Irma Agustina Lubis; Ridha Maya Faza Lubis
Journal of Computing and Informatics Research Vol 3 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v3i1.970

Abstract

The place of coffee sales, or more commonly known as a coffee shop, not only offers coffee but also serves a variety of hot and cold beverages. Many individuals, especially young people and students, choose to spend their time in modern coffee shops to sit and relax. Currently, coffee shops are often used as places for discussions, exchanging ideas, or simply relieving stress after activities. Coffee shops have become centers of social interaction with adequate service facilities. Although coffee shops are widespread, many people are not careful in choosing them. When choosing a coffee shop, it is important to select one that not only provides a comfortable environment but also serves the best-tasting coffee. The process of choosing the best coffee shop involves considerations such as price, taste quality, service, atmosphere, and cleanliness. To address this challenge, the author deems it essential to implement a Decision Support System (DSS). DSS is a field of science that utilizes technology to assist in problem-solving and accurate decision-making, without being manipulable. In the context of this research, the author uses the OCRA and ROC methods, as both are known as objective and easily understood methods. By applying the OCRA and ROC methods, the research results show that Gen’s Semar Cafe, with a score of 1.594, is selected as the best coffee shop.
Penerapan Metode Jaringan Saraf Tiruan Dalam Memprediksi Produksi Daging Domba Menurut Provinsi Listy Oktaviani; Sandy Erlangga; Bintang Aufa Sultan; Agus Perdana Windarto; Putrama Alkhairi
Journal of Computing and Informatics Research Vol 3 No 2 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v3i2.992

Abstract

Prediction is the process of estimating future needs. This research aims to predict the amount of sheep meat production by province. Lamb is a source of protein which is also a high value commodity. However, along with the increase in lamb production in Indonesia, the level of lamb meat consumption in Indonesia has tended to fluctuate in recent years. Imports are the step most often taken by the government to meet domestic sheep meat needs. By using Artificial Neural Networks and the backpropagation algorithm, the amount of sheep meat production will be predicted based on provinces in order to determine steps to fulfill domestic sheep meat needs based on the amount of sheep meat consumption in the community. This research uses data from 2001 to 2022 with 1 target, namely data for 2023.
Analisa Metode Backpropagation Dalam Memprediksi Jumlah Perusahaan Konstruksi Berdasarkan Provinsi di Indonesia Muhammad Kurniawansyah; Rafiqotul Husna; Raichan Septiono; Agus Perdana Windarto; Putrama Alkhairi
Journal of Computing and Informatics Research Vol 3 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v3i1.993

Abstract

This research aims to analyze the number of construction companies in Indonesia and gain an understanding of the trends and characteristics of the construction industry in that country. In this research, data related to the number of construction companies is analyzed using available sources such as government statistical reports, industry publications, and other secondary data sources. The data we use in this research is data on the number of construction companies by province in Indonesia from 2016-2021 which was taken from the website of the Central Statistics Agency (BPS) using the backprogation artificial neural network (JST) method. The analysis results show that the number of construction companies in Indonesia has increased significantly in recent years. It is hoped that this research will encourage strong economic growth and increasing investment in the infrastructure and property sectors has driven demand for construction services. In addition, government policies that support the construction sector, such as infrastructure development programs and regulations that facilitate foreign investment, also contribute to the growth in the number of construction companies. Apart from growth trends, this research also identifies several characteristics of the construction industry in Indonesia. The industry is dominated by small and medium-sized companies operating locally, although there are also large companies involved in large-scale projects. Competition in this industry is fierce, with companies vying to win construction contracts and develop a competitive advantage. The architectural models that we use in this research are 6 architectural models, of which the best architectural model will be obtained. The architectural models include 5-11-1-1 with an accuracy percentage of 61.8%, 5-12-1- 1 with an accuracy percentage of 70.6%, 5-14-1-1 with an accuracy percentage of 82.4%, 5-18-1-1 with an accuracy percentage of 64.7%, 5-20-1-1 with an accuracy percentage of 70.6%, 5-22- 1-1 with an accuracy percentage of 73.5%. So the best architectural model is obtained, namely the 5-12-1-1 model which produces an accuracy rate of 82.4%. with a Mean Square Error (MSE) of 0.00099997 with an error prone of between 0.001-0.05. These results are quite good in predicting the number of construction companies based on provinces in Indonesia
Implementasi Metode Tsukamoto Pada Sistem Pakar Diagnosa Kernikterus Defiyuliyanti Bazikho; Efori Buulolo; Meryance V. Siagian
Journal of Computing and Informatics Research Vol 3 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v3i1.1004

Abstract

Kernicterus is brain damage in infants due to high levels of bilirubin in the blood. Bilirubin is the cause of jaundice, and if left untreated, it can accumulate in the brain. The issue at hand is that the community, especially parents, still struggle to find a solution to determine the diagnosis of Kernicterus in a baby's body. Expert systems are advanced technology that can be used to address diagnostic problems with relevant accuracy. One of the expert system methods that can be employed for diagnosing diseases is Tsukamoto. Therefore, in this research, a expert system is built to obtain a Kernicterus diagnosis with relevant accuracy by implementing the Tsukamoto method to address the issues faced by patients. The results of this research show that testing the Tsukamoto method for diagnosing Kernicterus provides good accuracy. Hence, it can be concluded that implementing the Tsukamoto method in an expert system can be a solution to address the Kernicterus diagnosis problem

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