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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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Journal : JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING

 1 
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.
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
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.
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