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All Journal KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) JURIKOM (Jurnal Riset Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) KLIK: Kajian Ilmiah Informatika dan Komputer BEES: Bulletin of Electrical and Electronics Engineering Journal of Informatics, Electrical and Electronics Engineering
Eka Irawan
STIKOM Tunas Bangsa, Pematangsiantar
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Pengambilan Keputusan Dalam Menentukan Media Pembelajaran Online Pada Masa Pandemi Menggunakan Metode AHP dan TOPSIS Syahyulita Fachri; Eka Irawan; Ilham Syahputra Saragih
BEES: Bulletin of Electrical and Electronics Engineering Vol 2 No 2 (2021): November 2021
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (471.878 KB) | DOI: 10.47065/bees.v2i2.952

Abstract

STIKOM Tunas Bangsa is one of the computer science colleges located in Pematangsiantar City. Due to the Covid-19 Pandemic Period, the teaching and learning process is carried out online at their respective homes by students and lecturers. Sources of data obtained by interview and direct observation to students. The author's goal in this study is to determine the best learning media in the Information Systems Study Program at STIKOM Tunas Bangsa by using a combination of TOPSIS and AHP methods. With the given material variables, What'sApp variables, Classroom variables, and E-Learning variables, Google Meet variables and Zoom variables. The result of this study is the increase in the learning process and understanding of students during the Covid 19 Pandemic
Penerapan Metode Naive Bayes Dalam Menentukan Tingkat Kenyamanan Pada Rumah Sakit Terhadap Pasien Masduki Nizam Fadli; Irfan Sudahri Damanik; Eka Irawan
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 2 No. 3 (2021): Desember 2021
Publisher : STMIK Budi Darma

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Abstract

The level of security of a hospital is an important value that must be considered, because it can support a faster healing process for patients. To maintain the quality of comfort, doctors, nurses along with caricature in the hospital must always maintain the criteria determined by the hospital so that the better the value of the service. Understanding Comfort is a condition of feeling someone who feels comfortable based on the perception of each individual. Whereas comfort is a condition that has fulfilled the basic human needs of an individual nature due to several environmental conditions. In a large Indonesian dictionary, comfortable means fresh, healthy, delicious, cool, delicious. Naive Bayes is an algorithm of Data Mining where the nature of Naive Bayes itself is a classification which has two stages in the process of text classification, namely the training stage and the testing / classification stage. During the training phase, the process of analyzing the sample documents is in the form of vocabulary selection, which is a word that might appear in the collection of sample documents, as far as possible to be a representation of the document. Next is determining the prior probabilities for each category based on document samples
Penerapan Algoritma K- Medoids Dalam Mengelompokkan Tingkat Kasus Kejahatan di Setiap Provinsi Nur Arief; Irfan Sudahri Damanik; Eka Irawan
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 2 No. 3 (2021): Desember 2021
Publisher : STMIK Budi Darma

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Abstract

Drug abuse is a very serious problem and needs special attention from all parties. This is evidenced by the increasing number of drug cases and death cases due to the purchase of drugs issued from various media. The impact of drug addiction can be seen in one's physical, psychological and social. Drug abuse cases in each province in Indonesia have varying degrees of cases.This study aims to determine the cluster of drug abuse at high and low levels.The method needed for grouping drug case data is using data mining methods with the K-Medoids algorithm and using a computerized system that is rapidminer 5.3 application.The data used is sourced from the National Narcotics Agency of the Republic of Indonesia with the website: https://bnn.go.id/2015-2017 data which consists of 34 provinces to be divided into 2 clusters.From the calculation of the K-Medoids algorithm, high clusters were 10 provinces and low clusters were 24 provinces.This grouping is expected to be included for the government or related parties to further increase the socialization of the dangers of drugs in order to minimize mortality and crime due to drugs.
Sistem Pendukung Keputusan Pemilihan Promotor Vivo Terbaik (Studi Kasus : Pematangsiantar) Ali Akhbar Nasution; Saifullah Saifullah; Eka Irawan
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 4, No 1 (2020): The Liberty of Thinking and Innovation
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v4i1.2729

Abstract

Abstract− ecision support system is a computer-based system that can help decisions to solve certain problems by utilizing certain data and models. Many cases can be used as research in decision support systems, one of which is determining the best Vivo promoter. In this research, a decision making system will be designed using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. The TOPSIS method is a multi-criteria decision making method that uses the principle that the chosen alternative must have the shortest distance to the positive ideal solution and the farthest distance to the negative ideal solution. The steps used in the TOPSIS method are the normalization matrix calculation process, the weighted normalization matrix calculation process, the process of determining positive ideal solutions and negative ideal solutions, the process of calculating the distance of each alternative to the ideal solution, and the process of calculating the preference value of each alternative. The results obtained from this study are in the form of the best vivo Pramotor data in one month.Keywords: Decision Support System, TOPSIS, Best Vivo Pramotor
Implementasi Multifactor Evaluation Process (MFEP) Pada Pemilihan Foundation Bagi Perias Pemula Mawaddah Anjelita; Eka Irawan; Agus Perdana Windarto; Dedy Hartama; Irfan Sudahri Damanik
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 4, No 1 (2020): The Liberty of Thinking and Innovation
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v4i1.2697

Abstract

Abstract−This study aims to provide input, especially for novice make-up in choosing the best foundation, considering that foundation is one of the main components in make-up which is very influential in the perfection of make-up. The algorithm used in this study is the Multifactor Evaluation Process (MFEP). There are 5 factors used in the selection foundation, namely the durability of the foundation, the age of the product, the price, the color variants presented, and the coverage or ability of the foundation to cover deficiencies in the face. The foundation products used in this research are Lt-Pro Smooth Corector, Kryolan, Ultima II, Naturactor, and Este Lauder. The implementation of the Multifactor Evaluation Process (MFEP) in the selection of foundations for beginners can be applied. The result was that Kryolan foundation could be an alternative for beginners who were confused in choosing a foundation. The second order was obtained by Naturactor Foundation.Keywords: MFEP, Decision Support System, Multifactor Evaluation Process, Selection of Foundation, SPK
Penerapan Data Mining Dalam Mengelompokkan Calon Penerima Beasiswa Dengan Menggunakan Algoritma K-Means Nur Afriani Manihuruk; Muhammad Zarlis; Eka Irawan; Heru Satria Tambunan; Irawan Irawan
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 4, No 1 (2020): The Liberty of Thinking and Innovation
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v4i1.2575

Abstract

Penelitian ini bertujuan untuk mencari pengelompokkan data yang ada pada siswa yang berhak menerima beasiswa. Penyaluran beasiswa yang berasal dari keluarga yang kurang mampu harus dapat melalui seleksi yang melibatkan kriteria-kriteria tertentu. Kriteria tersebut seperti kondisi rumah, nilai raport, status rumah. Algoritma K-Means dapat membantu untuk mengklasifikasi siswa-siswi yang sangat layak untuk berupa mendapatkan bantuan berupa beasiswa. Adapun tujuan yang ada pada penelitian ini adalah menentukan clustering penerima beasiswa sehingga dapat memberikan rekomendasi yang layak, layak dengan pertimbangan dan kurang layak untuk menerima beasiswa dengan 4 kriteria. Data set yang digunakan sebanyak 128 siswa yang berasal dari sekolah SMP Muhammadiyah 54 Kerasaan. Data-data tersebut dapat dihitung dengan menggunakan algoritma K-Means dan pengujian dapat dilakukan melalui aplikasi RapidMiner 5.3. Metode K-Means berusaha mengelompokkan data yang ada kedalam beberapa kelompok, dimana data dalam satu kelompok mempunyai karakteristik yang sama satu sama lainnya dan mempunyai karakteristik yang berbeda dengan data yang ada didalam kelompok yang lain. Hasil penelitian diperoleh C1:73Item, C2:30Item, C3:25Item. Dari hasil analisis diharapkan dapat membantu pemahaman siswa yang berhak menerima beasiswa. Kata kunci: Data Mining, Metode K-Means, Pengelompokkan Penerima Beasiswa
Penerapan Data Mining Dalam Mengelompokkan Provinsi Rawan Kejahatan Menggunakan Algoritma K-Means Eka Desriani Aritonang; Heru Satria Tambunan; Jaya Tata Hardinata; Eka Irawan; Dedi Suhendro
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 4, No 1 (2020): The Liberty of Thinking and Innovation
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v4i1.2576

Abstract

Kemajuan Teknologi informasi saat ini berkembang sangat cepat yang mengakibatkan peningkatan pada data dalam jumlah besar. Meningkatnya jumlah kejahatan pada setiap provinsi di Indonesia menyebabkan penumpukan pada data. Beragam jenis kejahatan terjadi di lingkungan masyarakat, seperti pembunuhan, penganiayaan, pemerkosaan, pencurian, penipuan, penyalahgunaan narkoba, dan perjudian. Dengan melihat banyaknya jumlah kejahatan tersebut, masyarakat menjadi khawatir dan merasa tidak nyaman sehingga perlu dilakukan penelitian agar dapat mengetahui wilayah/provinsi yang rawan akan kejahatan. Tujuan dari penelitian yaitu sebagai referensi bagi pemerintah untuk meningkatkan keamanan untuk setiap wilayah pada tahun-tahun berikutnya. Penelitian ini menggunakan metode data mining dengan algoritma k-means clustering dan dibantu dengan aplikasi Rapidminer. Penelitian ini mengelompokkan provinsi dengan dua cluster yaitu cluster tinggi dan cluster rendah. Hasil dari penelitian ini diperoleh 4 provinsi dengan jumlah kejahatan tertinggi (C1), 30 provinsi dengan jumlah kejahatan rendah (C2) dan pengujian menggunakan Rapidminer mendapatkan hasil yang sama. Algoritma K-means dapat diterapkan dan memberikan informasi tentang provinsi yang rawan terjadinya kejahatan.Kata kunci: k-means, clustering, kejahatan
Analisis Prediksi Keterlambatan Pembayaran Listrik Menggunakan Komparasi Metode Klasifikasi Decision Tree dan Support Vector Machine Dinda Nabila Batubara; Agus Perdana Windarto; Eka Irawan
JURIKOM (Jurnal Riset Komputer) Vol 9, No 1 (2022): Februari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i1.3833

Abstract

Electrical energy is one of the most needed energy today. In this modern era, almost all human activities cannot be separated from the use of electricity. The only electricity supply company in Indonesia is the State Electricity Company or PT. PLN (Persero). PLN also has several obstacles. One of them is the very large amount of arrears to customers. This causes considerable losses for PLN. The electricity payment counter also experienced the same thing as experienced by PLN, as at PT Jaya Nuhgra Pratama. For this reason, this research was conducted by comparing two methods in the classification, namely decision tree and support vector machine to find out which method is the best in solving problems and to find out the factors that are the main causes of delays in electricity payments
Sistem Pakar dengan Proses Forward Chaining pada Kulit Wajah Berminyak Indah Syahputri; Agus Perdana Windarto; Dedi Suhendro; Eka Irawan; M Fauzan
Journal of Information System Research (JOSH) Vol 2 No 1 (2020): Oktober 2020
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

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Abstract

The research objective was to analyze oily facial skin using the forward chaining method and to build an expert system application that was able to provide accurate information about oily facial skin. Sources of data obtained by interviewing the owner of the House of Beauty who resides on Jl. Adam Malik, Pematangsiantar. The implementation of the expert system is a web application. The results showed that the system could be applied and analyzed oily facial skin using the forward chaining method, which was caused by various kinds of symptoms that attack everyone's facial skin, especially oily facial skin disease, based on the symptoms displayed by the user so that the expert system application could provide information about definition, treatment, and prevention, so as to help users in symptomatic symptoms and types of disease based on the symptoms that appear by the user.
Analisis Metode Vise Kriterijumska Optimizajica I Kompromisno Resenje (VIKOR) dalam Merekomendasikan Pupuk Terbaik bagi Produktivitas Tanaman Kelapa Sawit Nila Soraya Damanik; Eka Irawan; Muhammad Ridwan Lubis; Agus Perdana Windarto; Dedi Suhendro
Journal of Information System Research (JOSH) Vol 2 No 1 (2020): Oktober 2020
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (331.921 KB)

Abstract

The purpose of this study was to analyze and apply decision support system software in recommending the best fertilizer for oil palm productivity. Sources of data were obtained from PTPN IV Marihat through observations and interviews with PTPN IV Marihat regarding the data used. The solution given is a ranking using the Vise Kriterijumska Optimizajica I Kompromisno Resenje (VIKOR) method. In selecting the best fertilizer for oil palm plant productivity, several parameters of the assessment criteria are used, including soil type (C1), nutrient content (C2), dosage (C3), shape (C4), properties (C5), and price (C6). The alternatives used as a composition in selecting the best fertilizer for oil palm plant productivity are Urea Fertilizer, SP-36 Fertilizer, KCL Fertilizer, Dolomite Fertilizer, and Abu Janjang Fertilizer. The results of research conducted with the VIKOR method that ranking can be applied to selecting the best fertilizer for oil palm plant productivity where the best alternative with the smallest Q value is Dolomite Fertilizer with a value of Q = 0, the second alternative is Urea Fertilizer with a value of Q = 0.626, the third alternative is KCL fertilizer with a value of Q = 0.678, the fourth alternative is Ash Janjang Fertilizer with a value of Q = 0.833, and the last alternative is SP-36 Fertilizer with a value of Q = 1. It is hoped that the results of this research can help PTPN IV Marihat in recommend the best fertilizer so that it can increasing oil palm productivity at PTPN IV Marihat.
Co-Authors Agus Perdana Windarto Ali Akhbar Nasution Andi Sahputra Dedi Suhendro Dedi Suhendro Dedy Hartama Dinda Nabila Batubara Eka Desriani Aritonang Fitri Rizki Heru Satria Tambunan Ilham Syahputra Saragih Indah Syahputri Irawan Irawan Irfan Sudahri Damanik Jaya Tata Hardinata M Fauzan M Safii Masduki Nizam Fadli Mawaddah Anjelita Muhammad Ridwan Lubis Muhammad Zarlis Nazira Ayu Ananda Nila Soraya Damanik Nur Afriani Manihuruk Nur Arief Okprana, Harly Saifullah Saifullah Syahyulita Fachri Tanti Bonita