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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING METIK JURNAL Jurnal Mantik Brilliance: Research of Artificial Intelligence
ilham - sahputra
Universitas Malikussaleh
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Other

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Clustering Of Students Into A Specialization Of Expertise Using Genetic Algorithms ilham - sahputra
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 1 (2021): EDISI JULY 2021
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i1.5305

Abstract

Clustering mahasiswa kedalam keminatan keahlian merupakan salah satu upaya yang perlu dilakukan oleh pihak jurusan untuk menjamin mahasiswa memperoleh pendidikan yang sesuai dengan keahliannya. Saat ini, terdapat banyak metode clustering yang sudah dikembangkan oleh pakar. Umumnya metode clustering mampu mengelompokkan objek-objek yang memiliki tingkat kesamaan ciri yang tinggi, tetapi tidak mampu membatasi jumlah objek yang boleh masuk kedalam suatu kelompok. Kasus klasterisasi mahasiswa kedalam keminatan keahlian merupakan kasus clustering yang membatasi jumlah objek yang boleh masuk kedalam suatu kelompok. Dengan kondisi tersebut, metode clustering yang ada tidak dapat digunakan untuk kasus ini. Peneliti mencoba melihat kasus ini dari sudut pandang optimasi, yaitu bagaimana mengoptimalkan pembentukan kelompok keminatan mahasiswa dengan tingkat ketidaksesuaian bakat yang rendah. Untuk penyelesaian kasus ini, peneliti menggunakan algoritma genetika sebagai metode untuk penyelesaian masalah. Algoritma genetika dibagi kedalam beberapa jenis, yaitu: algoritma genetika dengan prinsip elitisme dan non elitisme, algoritma genetika dengan persentase mutasi 0.01, 0.03 dan 0.05. Berdasarkan penelitian yang dilakukan, diperoleh bahwa algoritma genetika mampu melakukan clustering mahasiswa kedalam keminatan keahlian yang disediakan oleh jurusan. Algoritma genetika dengan prinsip elitisme mampu menemukan solusi optimum yang lebih baik sebesar 39% dibandingkan dengan algoritma genetika non elitisme. Algoritma genetika dengan persentase mutasi 0.05 menghasilkan solusi optimum terbaik, namum memiliki konsumsi waktu yang paling besar dibandingkan dengan persentase 0.01 dan 0.03.
Implementation of Machine Learning in Determining Nutritional Status using the Complete Linkage Agglomerative Hierarchical Clustering Method Mutammimul Ula; Ananda Faridhatul Ulva; Mauliza Mauliza; Ilham Sahputra; Ridwan Ridwan
Jurnal Mantik Vol. 5 No. 3 (2021): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Problems that often occur in the nutritional status of children can be done prevention in the form of input to the people of north Aceh on the importance of fulfilling nutrition in toddlers in order to avoid stunting. Lack of nutrition is one of the causes of problems experienced by toddlers in north Aceh. The role of local governments, hospitals and health services is needed in looking at the amount of nutritional status of children, especially areas in northern Aceh. This research aims to be able to determine the nutritional status of toddlers and can provide convenience for hospital officials and doctors in handling gradually and how to treat on a scale in diagnosing diseases with child nutritional status. The first method of this study is to group toddlers identified nutritional status of children who are classified as stunting or not and then grouped areas that are malnourished children using hierarchical agglomerative models. The results of this study can diagnose nutritional status in children with Machine Learning using complete linkage agglomerative hierarchical clustering whose final results can see areas prone to stunting. The data to be modeled consists of 12 sub-districts with samples taken in the form of the number of cases of baktiya 12, dewantara 21, kuta makmur 83, meurah mulia 84, jambo aye 87, nibong 83, sacred store 68. the process of complete linkage agglomerative hierarchical clustering Baktiya method from Scaling Data (standardization)-1.344354111, Kuta Makmur1.376783706, Meurah Mulia 1.415109591, Cot Girek -0.462858762, Simpang Kramat0.801895435, Nisam Antara0.648591896. Based on the results of distance calculations, Prosedure was carried out up to 11 times resulting in cluster groups of 3,21,7,14.15 with a result of 0, clusters 17,23,8,13,18,20,11 with results of 1.6628305 and 1.4,10,19,26,2,9,5,12 with a value of 2.720995. The final calculation of 19,26,1,4,10 is 2.11633.
APPLICATION OF MACHINE LEARNING WITH THE BINARY DECISION TREE MODEL IN DETERMINING THE CLASSIFICATION OF DENTAL DISEASE Mutammimul Ula; Fajar Tri Tri Anjani; Ananda Faridhatul Ulva; Ilham Sahputra; Angga Pratama
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 6, No 1 (2022): Issues July 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i1.7341

Abstract

The dangers of health problems in dental disease are common for children and adults. Many dental problems get priority treatment based on data from Riskesdas, about 67.6% of the Indonesian population suffers from dental and oral problems. This affects other parts of the organ that are interrelated. Therefore, this study formulates how to solve the determination of dental disease, by applying the UDB model in machine learning. The purpose of this study was to determine the application of machine learning Binary Decision Tree (BDT) in the classification of classified dental diseases identified by decision trees in determining the results of dental disease predictions including groups and how to solve them. The research methodology in the first stage of data collection was carried out directly with the dental clinic at Cut Meutia Lhokseumawe Hospital. Then input the dental disease data along with the dental disease symptom data. The final stage is dividing the attribute values in viewing the value at a predetermined branch which is then in the form of a decision tree as a reference for the final prediction. The results of the assessment have each value indicating a high level of accuracy, with an accuracy of 92 percent and an inaccuracy of 8 percent of the 40 data points tested. Furthermore, the conclusion of this study can produce an appropriate classification of dental disease and is able to produce accurate results seen from a small error rate
Clustering Analysis and Mapping of ISPA Disease Spread Patterns in Bireuen District Mutammimul Ula; Tsania Asha Fadilah Daulay; Richki Hardi; Sujacka Retno; Angga Pratama; Ilham Sahputra
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.4936

Abstract

ISPA disease can be detected by analyzing the regional distribution map of the disease. Early detection of ARI is very important for effective prevention. The study conducted in Bireuen Regency used data from 2019 to 2021, sourced from dr. Fauziah Bireuen Hospital, revealed that there was an increase in ARI cases from an average of 13.18 to 59.24 per year. The aims of the study were to identify ARI clusters, analyze disease patterns using Spatial Pattern Analysis and Flexible Shaped Spatial Scanning Statistics. The methodology involves collecting patient data for each ARI case and processing it using DBSCAN to obtain cluster points on the map. Spatial Pattern Analysis is used to analyze these clusters and identify hotspot points on the map. The analysis resulted in four clusters: Cluster 1 (6 subdistrict), Cluster 2 (4 subdistrict), Cluster 3 (1 subdistrict), and Cluster 4 (6 subdistrict). The study identified 6 hotspots in 2019, 5 hotspots in 2020, and 6 hotspots in 2021. Each ARI disease clustering map shows the distribution of ARI cases and identifies areas prone to the disease. These findings provide valuable insights for targeted interventions and preventive actions in identified high-risk areas of ISPA.
Implementasi Algoritma C5.0 Pada Klasifikasi Status Gizi Ibu Hamil di Kota Lhokseumawe Ilham Sahputra; Mauliza Mauliza; Siti Fatimah A Zohra
METIK JURNAL Vol 7 No 1 (2023): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v7i1.562

Abstract

Status gizi ibu hamil dapat mempengaruhi pertumbuhan janin yang sedang dikandung, sehingga penentuan status gizi bagi ibu hamil menjadi sangat penting agar seorang ibu dapat menyesuaikan kondisi kesehatannya dengan baik. Penelitian ini bertujuan untuk menerapkan algoritma C5.0 pada klasifikasi status gizi ibu hamil. Penelitian ini menggunakan 355 dataset ibu hamil yang diperoleh dari beberapa puskesmas di kota Lhokseumawe. Penelitian ini bertujuan untuk menerapkan algoritma C5.0 untuk melakukan klasifikasi status gizi ibu hamil berdasarkan data yang diperoleh dari beberapa puskesmas yang berada di kota Lhokseumawe. Data primer yang diperoleh dari lapangan sebanyak 355 data yang terdiri dari 9 feature diantaranya: Umur, BB Dulu, BB Sekarang, TB, LiLA, Tekanan Darah, HB, IMT, dan BB Ideal. Sementara kelas data terdiri dari 2 kategori yaitu: gizi kurang dan gizi normal. Berdasarkan penelitian yang dilakukan, diperoleh hasil bahwa metode C5.0 mampu bekerja dengan sangat baik dalam melakukan klasifikasi data status gizi ibu hamil. Akurasi yang dihasilkan mencapai 94,33%, presisi 95%, recall 97%, dan f-1 score 96% dengan perbandingan jumlah data latih dan data testing adalah 70 : 30.
The Nutritional Classification of Pregnant Women Using Support Vector Machine (SVM) ilham - sahputra; Bustami Bustami; Cut Farida Aryani
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 7 No. 1 (2023): Issues July 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v7i1.9764

Abstract

Determining the nutritional status of pregnant women is one of the efforts to control the condition of pregnant women so that they can adjust their health conditions properly. The health condition of pregnant women can affect the condition of the baby who will be born. This study aims to apply the SVM method to a web-based application to classify the nutritional status of pregnant women based on data obtained from several health centers in the city of Lhokseumawe. SVM functions as the core of the application in charge of classifying the nutritional status of pregnant women based on several features including: age, weight, height, lila, hemoglobin and BMI. While the data class consists of 3 categories, namely: undernourished, normal nutrition and normal nutrition + overweight. Primary data obtained from the field amounted to 355 data which were then divided into two parts with a ratio of 70% training data and 30% testing data. Based on the research conducted, it was found that the application of different kernels in the Support Vector Machine (SVM) will have a different performance impact in classifying data. In this study, the linear kernel has the best performance with an accuracy value of 0.84, the RBF kernel has an accuracy value of 0.83, the polynomial kernel has an accuracy value of 0.72, and the sigmoid kernel has the worst performance with an accuracy value of 0.58
Design and Construction of 2.4 Ghz Omnidirectional Antenna as Wireless LAN Transmitter (Case Study at Bukit Indah Campus, Malikussaleh University) Rizal Tjut Adek; Rahmat Rinaldi; Ilham Sahputra; Mukhlis
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 7 No. 1 (2023): Issues July 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v7i1.9820

Abstract

Communication with a wireless system is one of the mainstay communications so that communication integration is achieved. For this reason, Malikussaleh University provides a wifi network on each campus, one of which is the Bukit Indah campus. So that the achievement of wifi access points on mobile devices needs to be improved so that it is achieved properly. Therefore it is necessary to optimize the placement of access points on the Malikussaleh University wifi network. Modeling an omnidirectional antenna that has a range of 360 ° is one solution. The method applied is to design and build an omnidirectional antenna on the Bukit Indah Campus of Malikussaleh University. Based on simulations on Mobile Radio, the largest network pathloss is 116.2 dB and the smallest is 102.0 dB. The results of omidirectional antenna testing conducted 5 times obtained the largest download speed worth 3.18 Mbps with 314ms ping at a distance of 30 meters and the smallest download speed worth 0.55Mbps with 195ms ping at a distance of 150 meters. The farther the distance of the antenna the lower the ping and packet loss, from the test results obtained an average ping of 267.8ms and the largest packet loss percentage of 0.4%. The ideal distance for this antenna is 30 to 110 meters and can even get a longer distance provided there are no obstacles. With this distance it is ideal for an assembled antenna with makeshift materials that we often encounter in everyday life.
Development of an Intelligent System to Determine Land Suitability for Horticultural Crops on Vegetable Commodities Ilham Sahputra; Usnawiah; Rizky Putra Fhonna; Dinda Saima Agustina Siregar; Difa Angelina
Brilliance: Research of Artificial Intelligence Vol. 3 No. 2 (2023): Brilliance: Research of Artificial Intelligence, Article Research November 2023
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v3i2.3100

Abstract

Global climate change has a significant impact on the agricultural sector, including horticulture, with climate fluctuations such as increased temperatures and changes in rainfall patterns potentially affecting crop productivity. Sustainable horticultural agriculture is important for safeguarding natural resources and reducing environmental impacts. However, challenges from climate change and variations in land conditions can affect horticultural crop production. Identifying crops that are suitable for the climate and land conditions is key to agricultural sustainability. An intelligent and adaptive approach is needed in selecting the right crops to grow in the face of climate change. This research develops an artificial intelligence application for the recommendation of horticultural crop types according to land conditions and climate change. The model built involves AHP and MFEP methods. The model takes into account various land parameters with weights determined through the AHP approach, allowing this AI application to provide accurate recommendations based on data and modeling. Based on the tests conducted, the system was able to produce analysis with an accuracy rate of 85%.
Co-Authors Ananda Faridhatul Ulva Angga Pratama Bustami Bustami Cut Farida Aryani Difa Angelina Dinda Saima Agustina Siregar Fajar Tri Tri Anjani Mauliza Mauliza Mauliza, Ade Mukhlis Mukhlis Mutammimul Ula Rahmat Rinaldi Richki Hardi Ridwan Ridwan Rizal S.Si., M.IT, Rizal Rizky Putra Fhonna Siti Fatimah A Zohra Sujacka Retno Tsania Asha Fadilah Daulay Usnawiah