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All Journal Jurnal Eksplora Informatika JURNAL ILMIAH INFORMATIKA KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) J-Icon : Jurnal Komputer dan Informatika
Dedy Hartama
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Penerapan Algoritma C4.5 dalam Menentukan Faktor yang Dapat Meningkatkan Kemampuan Bahasa Inggris pada Mahasiswa Septri Wanti Siahaan; Kristin Daya Rohani Sianipar; P.P.P.A.N.W Fikrul Ilmi R.H Zer; Dedy Hartama
Jurnal Eksplora Informatika Vol 10 No 1 (2020): Jurnal Eksplora Informatika
Publisher : Bagian Perpustakaan dan Publikasi Ilmiah - Institut Teknologi dan Bisnis STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/eksplora.v10i1.396

Abstract

Negara-negara di dunia memiliki bahasa tersendiri. Bahasa yang paling dominan digunakan adalah Bahasa Inggris. Oleh karena itu, Bahasa Inggris merupakan Bahasa Internasional. Bahasa internasional adalah bahasa yang digunakan agar dapat berkomunikasi dengan orang-orang di dunia. Indonesia merupakan salah satu negara yang menerapkan Bahasa Inggris sebagai mata pelajaran atau mata kuliah wajib yang dipelajari oleh siswa/i dan mahasiswa/i. Mahasiswa/i wajib mengetahui bahwa mempelajari Bahasa Inggris sangat dibutuhkan untuk meningkatkan kompetensi di masa depan. Di era globalisasi saat ini, Bahasa Inggris dapat memberikan dampak yang mendominasi seperti komunikasi, pekerjaan, studi, dan lainnya. Namun, seperti yang semua orang tahu bahwa, mempelajari Bahasa Inggris tidaklah mudah. Banyak mahasiswa/i yang mendaftar kelas Bahasa Inggris untuk meningkatkan kemampuan Bahasa Inggris. Namun, ada juga mahasiswa/i yang tidak mendaftar kelas Bahasa Inggris tetapi, memiliki kemampuan Bahasa Inggris yang bagus. Dalam menyelesaikan penelitian ini, penulis menerapkan Algoritma yang ada pada Data Mining yaitu Algoritma C4.5. Hasil dari penelitian ini adalah faktor “Mendengar dari Lingkungan” mendapatkan gain tertinggi pada iterasi ke-1. Kesimpulan dari penelitian ini adalah bahwa faktor yang mempengaruhi dalam meningkatkan kemampuan Bahasa Inggris pada mahasiswa adalah “Mendengar dari Lingkungan”. Penelitian ini memiliki tujuan untuk menentukan faktor yang meningkatkan kemampuan Bahasa Inggris pada mahasiswa.
PREDIKSI PRODUKSI SUSU SEGAR DI INDONESIA MENGGUNAKAN ALGORITMA BACKPROPAGATION Jonas Rayandi Saragih; Dedy Hartama; Anjar Wanto
JURNAL ILMIAH INFORMATIKA Vol 8 No 01 (2020): Jurnal Ilmiah Informatika (JIF)
Publisher : Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (473.143 KB) | DOI: 10.33884/jif.v8i01.1847

Abstract

Milk is a white liquid produced from female mammals that contain carbohydrates that are useful for humans. Based on data from the Indonesian Statistics Agency, milk productivity in Indonesia from 2012 to 2018 experienced an unstable curve. Therefore this research was conducted to predict and find out the level of development of milk productivity in Indonesia for the following years, so that companies that use milk have a reference to continue to strive to increase milk productivity in Indonesia to remain stable in order to meet the needs of the community and minimize milk imports. This algorithm used is backpropagation neural network. This algorithm is able to predict good data especially data that is sustainable in a certain period of time. to simplify this research the author uses the Matlab 2011 application. To facilitate writers, authors use 5 architectural model, namely 5-9-1 = 94%, 5-12-1 = 88%, 5-14-1 = 88%, 5-15-1 = 94%, 5-17-1 = 94 %. So we get the best architectural model using the architectural mode 5-15-1 with an accuracy rate of 94% with MSE = 0,000999842. Finally, this model is good enough to predict fresh milk production by province in Indonesia
PENERAPAN K-MEDOID PADA RUMAH TANGGA YANG MEMILIKI SUMBER PENERANGAN LISTRIK PLN BERDASARKAN PROVINSI Cici Astria; Agus Perdana Windarto; Dedy Hartama
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 3, No 1 (2019): Smart Device, Mobile Computing, and Big Data Analysis
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v3i1.1667

Abstract

Electricity has now become something that is very much needed in daily life, every household has used PLN electricity lighting sources. However, there are also some regions in Indonesia that have not been able to enjoy good and even electric lighting. The data source used from the Indonesian Statistics Agency website is the Percentage of Household Data by Province and Treatment, 2013-2014. This study aims to classify households that have a source of electricity for PLN using the datamining algorithm with K-Medoid. The data is processed into 2 clusters, namely high level clusters (C1) and low level clusters (C2). Where the results of this study concluded from 33 provinces in Indonesia that the cluster level of low waste sorting behavior (C1) obtained 11 provinces namely Aceh, Kep. Bangka Belitung, DKI Jakarta, West Java, Central Java, DI Yogyakarta, East Java, Banten, Bali, West Nusa Tenggara, and North Sulawesi and 23 other provinces are included in the low-level cluster (C2).Keywords: Electricity, Datamining, K-Medoid, Clustering, Household Sources
ANALISA METODE DATA MINING PADA PENGELOMPOKAN LAPANGAN KERJA INFORMAL SEKTOR NON-PERTANIAN Khairunnissa Fanny Irnanda; Agus Perdana Windarto; Dedy Hartama; Anjar Wanto
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 3, No 1 (2019): Smart Device, Mobile Computing, and Big Data Analysis
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v3i1.1673

Abstract

The objective of the study is to classify informal employment in non-agricultural sectors. Data sources are obtained from the Central Statistics Agency (BPS). The data used is the proportion of employment for informal non-agricultural sectors (2015-2018), consisting of 34 Provinces in Indonesia. The Method used to solve the problem is datamining technique K-Medoid. The results of the research showed that the percentage of informal employment of non-agricultural sectors based on the lowest region became a record for the government to further increase human resources and more open the field jobs in non-agricultural sectors, among others.Keywords: Informal sector, Datamining, K-Medoid, Clustering, Non-Agricultural
IMPLEMENTASI ALGORITMA SMITH PADA APLIKASI ISTILAH TEKNIK INDUSTRI KIMIA Nasib Marbun; Ahmad Riza Fuadi; Muasir Pagan; Suranta Bill Fatric Ginting; Muhammad Zarlis; Dedy Hartama
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 3, No 1 (2019): Smart Device, Mobile Computing, and Big Data Analysis
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v3i1.1637

Abstract

Companies engaged in the chemical industry really need Human Resources (HR) who have knowledge in the field of chemical industry engineering terms in order to achieve the company's main goals, but due to the chemical industry engineering terms that use foreign languages is essentially difficult to understand not a few of the general public who have desire to work in companies in the chemical industry engineering experience problems in mastering the knowledge needed by chemical industry engineering companies. Therefore it is necessary to have an application that adopts science related to the chemical industry engineering term along with good search features. The application that was built to search for information related to chemical industry engineering terms in this study implements the smith algorithm as an algorithm that serves to minimize the use of the time needed in the search process. The smith algorithm notices that counting the number of shifts with the text character that is located right next to the window character on the rightmost text can sometimes provide a shorter shift than using the rightmost window text character. Then he gives a solution to take the maximum between the two values. The application term of chemical industry engineering research was built using visual basic net 2008 and can be operated properly on personal computers supported by Windows OS. Keywords: Chemical Industry Engineering Terms, String Matching, Algorithms, Smith.
PENERAPAN DATAMINING DENGAN METODE KLASIFIKASI UNTUK STRATEGI PENJUALAN PRODUK DI UD.SELAMAT SELULAR Sinta Maulina Dewi; Agus Perdana Windarto; Dedy Hartama
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 3, No 1 (2019): Smart Device, Mobile Computing, and Big Data Analysis
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v3i1.1669

Abstract

In the current era of globalization, developments in various fields of business are accelerating. Both in the culinary field and other fields. One of the most sought after business developments is in the field of counters or credit sales. UD.Selamat Selular was founded in 2010, which only has a small shop with no employees to date which has more than 20 employees. This business continues to develop in ever-increasing business competition. Therefore a sales strategy is needed so that it is not inferior to other trading businesses. In this research, it is necessary to test the previous data in order to find out the right sales strategy using Naïve Bayes. The data collection method was conducted by questionnaire and interview with a questionnaire of 160 respondents. From the results of this study it can be concluded that the model formed using the Naïve Bayes algorithm produces an algorithm of 0.650 so that it is classified as Excellent Classification.Keywords: Datamining, Naïve Bayes, Sales Strategy.
ANALISA METODE DATA MINING PADA PRODUKSI PERIKANAN LAUT YANG DIJUAL DI TEMPAT PERIKANAN IKAN (TPI) Hanifah Urbach Sari; Agus Perdana Windarto; Dedy Hartama
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 3, No 1 (2019): Smart Device, Mobile Computing, and Big Data Analysis
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v3i1.1671

Abstract

The purpose of this research is that the results of the utilization of fish resources in producing marine fisheries by fishermen can be good using the K-Means clustring method. Data was obtained from the Central Statistics Agency (BPS) and assisted using RapidMiner software. Data used from 2013-2017 consisted of 21 Provinces. With these data can be obtained data with high-level clusters (C1), namely Central Java with production 587002.8 and low-level clusters (C2) provinces of Aceh, North Sumatra, West Sumatra, Bengkulu, Lampung, Bangka Belitung Islands, DKI Jakarta, West Java , DI Yogyakarta, East Java, Banten, Bali, West Nusa Tenggara, West Kalimantan, Central Kalimantan, North Sulawesi, Central Sulawesi, South Sulawesi, Southeast Sulawesi and Gorontalo with a production of 20302.28. This can be input to the government for provinces that have low water catchment areas to be of more concern based on the cluster that has been done.Keywords: K-Means, Sea Fish Production, Clustering, Territory
Penentuan Keberhasilan Pembelajaran Daring Pada Masa Pandemi Covid-19 dengan Menggunakan Algoritma C4.5 di Stikom Tunas Bangsa Eko Ahadi; Indra Gunawan; Ika Okta Kirana; Dedy Hartama; Muhammad Ridwan Lubis
J-ICON : Jurnal Komputer dan Informatika Vol 10 No 1 (2022): Maret 2022
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v10i1.6446

Abstract

The C4.5 algorithm is an algorithm for classifying, grouping and predicting. The calculation of the RapidMiner C4.5 algorithm produces the same results according to the decision tree in the case of online learning success. Manual calculations using RapidMiner produce 23 successful online learning rules. Decision trees make it easier to understand the attribute that is prioritized as the most important attribute, root class and leaf class. The C4.5 algorithm as the application of data mining can result in successful online learning decisions during the Covid-19 pandemic. The decision tree in the case of the success of online learning during the Covid-19 pandemic is said to be successful.
Co-Authors Agus Perdana Windarto Ahmad Riza Fuadi Ambariyanto Ambariyanto Cici Astria Eko Ahadi Hanifah Urbach Sari Ika Okta Kirana Indra Gunawan Jonas Rayandi Saragih Khairunnissa Fanny Irnanda Kristin Daya Rohani Sianipar Muasir Pagan Muhammad Ridwan Lubis Muhammad Zarlis Nasib Marbun P.P.P.A.N.W Fikrul Ilmi R.H Zer Septri Wanti Siahaan Sinta Maulina Dewi Suranta Bill Fatric Ginting Wanto, Anjar