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JOMLAI: Journal of Machine Learning and Artificial Intelligence

jomlai
Website

Published by Yayasan Literasi Sains Indonesia

ISSN : 28289102 EISSN : 28289099 DOI : 10.55123/jomlai

Core Subject : Science, Engineering,

Computer Science & IT Engineering

Focus and Scope JOMLAI: Journal of Machine Learning and Artificial Intelligence is a scientific journal related to machine learning and artificial intelligence that contains scientific writings on pure research and applied research in the field of machine learning and artificial intelligence as well as an overview of the development of theories, methods, and related applied sciences. Topics cover the following areas (but are not limited to): Software engineering Hardware Engineering Information Security System Engineering Expert system Decision Support System Data Mining Artificial Intelligence System Computer network Computer Engineering Image processing Genetic Algorithm Information Systems Business Intelligence and Knowledge Management Database System Big Data Internet of Things Enterprise Computing Machine Learning Other relevant study topics Noted: Articles have primary citations and have never been published online or printed before

Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan

Articles 86 Documents

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Diabetes Risk Prediction using Logistic Regression Algorithm Qatrunnada Refa Cahyani; Mochammad Januar Finandi; Jathu Rianti; Devi Lestari Arianti; Arya Dwi Pratama Putra
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 2 (2022): June
Publisher : Yayasan Literasi Sains Indonesia

Many factors affect people suffering from diabetes, some of which are high blood pressure, excess sugar levels, weight, genetic history of diabetes, age, number of pregnancies, skin fold thickness, and the amount of insulin levels in the body. Logistic regression is a statistical tool that can be used in classification modeling about the presence or absence of diabetes. The aim of this study is to predict diagnostically whether a patient has diabetes or not. The results obtained are relatively low predictions because the ranges of values of several factors that cause it are very far apart so normalization is carried out so that the ranges of values are close together. The result is that diabetes risk prediction using a logistic regression algorithm with normalization resulted in a recall of 55% while without normalization it was 43%. Thus, normalization can improve the performance of diabetes risk prediction using a logistic regression algorithm. This model is expected to be a reference for the treatment of diabetics for doctors in hospitals and in the community to find out how to maintain a lifestyle and how to avoid diabetes in terms of the variables that affect the occurrence of the disease.

Implementation of K-Means Algorithm for Clustering Books Borrowing in School Libraries Daud Siburian; Sundari Retno Andani; Ika Purnama Sari
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 2 (2022): June
Publisher : Yayasan Literasi Sains Indonesia

The school library is an important resource in an effort to support the process of improving the quality of education in schools. Through the library a lot of information can be extracted and used for educational purposes. The library is expected to play its function as a vehicle for education, research, preservation, information, and recreation to improve the nation's intelligence. This study aims to cluster the borrowing of library books at SMA Assisi Pematangsiantar. The research data was obtained from the school library. The algorithm used for the clustering process is K-Means Clustering which is one of the data mining algorithms. The data was processed using Microsoft Excel and Rapid Miner 5.3 to determine the value of the centroid in 2 clusters, namely the highest and lowest clusters. Based on manual calculations with Microsoft Excel and testing with Rapid Miner, this study resulted in the same value, namely the highest cluster produced 6 types of books including Mathematics,. Geography, Chemistry, Civics, Physical Education and Computers. As for the lowest cluster, there are 6 types of books, namely Indonesian, English, Biology, Physics, Religion and Cultural Arts. So it can be concluded that the K-Means method in this study can cluster school library book borrowing well, referring to manual calculations and testing which have the same results

Application of Artificial Bee Colony Algorithm to Optimize The Shortest Route to Distribute Clean Water Pipes Mhd Furqan; Yusuf Ramadhan Nasution; Khairunnisa Khairunnisa
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 2 (2022): June
Publisher : Yayasan Literasi Sains Indonesia

Regional Water Company Tirtanadi is a company engaged in clean water treatment and distributing clean water into the customers’ houses. The pipelines used are long and branched which causes the water volume to be divided. So, the alternative solution offered, to make sure the water distribution to each customers’ houses are efficient, is to search the shortest route using the Artificial Bee Colony algorithm. Artificial Bee Colony algorithm is a metaheuristic algorithm which has a strong global search ability and is able to solve continuous problems on determining the optimal clean water pipe route. This research’s goal is to facilitate Tirtanadi company on deciding the best installation point for the clean water distribution pipe. This research uses a dataset in the form of 8 installation points and one water treatment plant point. According to the calculation result on determining the best water pipe route using the Artificial Bee Colony algorithm obtained an optimal route which is V1→V7→V4→V9→V8→V2→V6→V3→V5. So it can be concluded that Artificial Bee Colony Algorithm is able to decided the search for clean water distribution pipes route on PDAM Tirtanadi and is able to give a good solution for searching for the shortest route.

Optimization of Computer Network Security System Against Malware Attacks Using Firewall Filtering with Port Blocking Method Andri Andri; Indra Gunawan; Ika Okta Kirana
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 2 (2022): June
Publisher : Yayasan Literasi Sains Indonesia

Computer networks have an important role in teaching and learning activities in schools, but there are also negative impacts. One of them is prone to attack by malware such as viruses and so on, as is the case at the Satria Mandiri Private Vocational School in Bandar Huluan. So far, the computer network at the school is very easy to attack by malware. The negative impact of malware on the network is bandwidth traffic overload, causing bandwidth constraints to run out quickly or data transfer traffic to be slower than usual. The reliability of a network can be determined from the security factor of the network itself. Some routers have firewall settings that are quite capable but need to be managed more specifically based on the needs of the 1500 Kbps network scale and available bandwidth. Creating good rules in the firewall will make it easier to filter network traffic and bandwidth so that it can create security and convenience for network and bandwidth users. Port blocking allows users or users to interact with the proxy server on the local network, where the connected user has gone through verification that can filter malware activity with embedded rules

Identification of Rice Plant Diseases Through Leaf Image Using DenseNet 201: Identifikasi Penyakit Tanaman Padi Melalui Citra Daun Menggunakan DenseNet 201 Primatua Sitompul; Harly Okprana; Annas Prasetio
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 2 (2022): June
Publisher : Yayasan Literasi Sains Indonesia

Indonesia as an agrarian country with the largest population uses rice as a staple food is depending on rice production. The lack of quantity and quality of production that often occurs is caused by disease attacks on plants that are detected too late. This is due to the lack of agricultural extension workers who help farmers in dealing with plant diseases. This study conducted an experiment on rice plant diseases based on leaf imagery using a dataset that has four classifications of leaf conditions of rice plants, namely healthy, brown spot, hispa, and leaf blast. The results obtained are quite good, namely, the accuracy value of the training data is 88.4% and 82.99% in data testing using the Densely Connected Convolutional Networks (DenseNet)-201 architecture as. From the results of the key research, DenseNet201 is quite suitable to be used to carry out diseases in rice plants so that the types of diseases that attack can be identified and given early. Thus food security can be maintained and not cause losses due to crop failures that harm farmers.

Application of Artificial Neural Networks to Predict Exports of Four-Wheeled Vehicles by Destination Country Ema Meyliza; Eka Irawan; Dedi Suhendro
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 3 (2022): September
Publisher : Yayasan Literasi Sains Indonesia

Motorized vehicles are vehicles that are energized through machines and used for land transportation,, as well as movement through technical equipment in the form of electric motors and other tools that have the function of converting an energy source into power to drive the vehicle. This study aims to determine the results of the predicted number of four-wheeled vehicle exports by destination country in the years to come. The research data used is data on exports of four-wheeled motor vehicles by destination country for 2012-2020. The algorithm used in this research is an artificial neural network with Back-propagation method. The best architectural model used in this research is architect 4-4-1 with an accuracy rate of 82% epoch of 2261 iterations and MSE of 0.0081876. So it can be concluded that the model can be used to predict export data for four-wheeled vehicles by destination country.

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

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

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

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%.

Application of Artificial Neural Networks in Predicting Salt Imports by Country of Origin Using the Back-propagation Method Sari Marito Tondang; Heru Satria Tambunan; Susiani Susiani
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 3 (2022): September
Publisher : Yayasan Literasi Sains Indonesia

Salt is a basic consumption material needed by the community and various industries. Indonesia is a country that has many beaches that have great potential as a source of salt production. But Indonesia is still dependent on imports so that industrial imports continue to increase, can directly or indirectly affect the risk of the country’s economic pattern. An increase in salt imports although there was also a decrease but only slightly and did not last long from several countries from 2010-2020 recorded in the Central Statistics Agency (BPS). In this study, the author will predict the import of salt for the next 3 years using the Back-propagation algorithm. Back-propagation is one of the artificial neural network methods that is quite reliable in solving problems where the network tries to achieve stability again to achieve the expected output and there is a learning process by adjusting connection weights. This study uses 6 architectural models : 5-80-1, 5-90-1, 5-100-1, 5-110-1, from the four models the best architectural model is obtained namely 5-90-1 with an accuracy value of 75%, epoch 4265 iterations, and MSE Testing 0,01569.

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name
Anjar Wanto

Contact Email
anjarwanto@ieee.org

Phone
+6282294365929

Journal Mail Official
jomlai.journal@gmail.com

Editorial Address
Jl. Bunga Cempaka No. 51D. Medan. Indonesia Phone: +62 822-9436-5929 | +62 812-7551-8124

Location
Kota medan,

Sumatera utara

INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Anjar Wanto

Contact Email anjarwanto@ieee.org

Phone +6282294365929

Journal Mail Official jomlai.journal@gmail.com

Editorial Address Jl. Bunga Cempaka No. 51D. Medan. Indonesia Phone: +62 822-9436-5929 | +62 812-7551-8124

Location Kota medan, Sumatera utara INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Anjar Wanto

Contact Email
anjarwanto@ieee.org

Phone
+6282294365929

Journal Mail Official
jomlai.journal@gmail.com

Editorial Address
Jl. Bunga Cempaka No. 51D. Medan. Indonesia Phone: +62 822-9436-5929 | +62 812-7551-8124

Location
Kota medan,

Sumatera utara

INDONESIA