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All Journal InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan) Jurnal Informatika Universitas Pamulang JurTI (JURNAL TEKNOLOGI INFORMASI) Petir IJISTECH (International Journal Of Information System & Technology) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Jurnal Mantik IJISTECH
Dedy Hartama
STIKOM Tunas Bangsa Pematangsiantar
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences

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Use of Binary Sigmoid Function And Linear Identity In Artificial Neural Networks For Forecasting Population Density Anjar Wanto; Agus Perdana Windarto; Dedy Hartama; Iin Parlina
IJISTECH (International Journal of Information System and Technology) Vol 1, No 1 (2017): November
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v1i1.6

Abstract

Artificial Neural Network (ANN) is often used to solve forecasting cases. As in this study. The artificial neural network used is with backpropagation algorithm. The study focused on cases concerning overcrowding forecasting based District in Simalungun in Indonesia in 2010-2015. The data source comes from the Central Bureau of Statistics of Simalungun Regency. The population density forecasting its future will be processed using backpropagation algorithm focused on binary sigmoid function (logsig) and a linear function of identity (purelin) with 5 network architecture model used the 3-5-1, 3-10-1, 3-5 -10-1, 3-5-15-1 and 3-10-15-1. Results from 5 to architectural models using Neural Networks Backpropagation with binary sigmoid function and identity functions vary greatly, but the best is 3-5-1 models with an accuracy of 94%, MSE, and the epoch 0.0025448 6843 iterations. Thus, the use of binary sigmoid activation function (logsig) and the identity function (purelin) on Backpropagation Neural Networks for forecasting the population density is very good, as evidenced by the high accuracy results achieved.
PENERAPAN DATAMINING PADA POPULASI DAGING AYAM RAS PEDAGING DI INDONESIA BERDASARKAN PROVINSI MENGGUNAKAN K-MEANS CLUSTERING Mhd Gading Sadewo; Agus Perdana Windarto; Dedy Hartama
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 2, No 1 (2017): InfoTekJar September
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v2i1.164

Abstract

Ayam bukanlah makanan yang asing bagi penduduk Indonesia. Makanan tersebut sangat mudah dijumpai dalam kehidupan masyarakat sehari-hari. Namun tingkat konsumsi daging ayam di Indonesia masih tergolong rendah dibandingkan dengan Negara tetangga. Penelitian ini membahas tentang Penerapan Datamining Pada Populasi Daging Ayam Ras Pedaging di Indonesia Berdasarkan Provinsi Menggunakan K-Means Clustering. Sumber data penelitian ini dikumpulkan berdasarkan dokumen-dokumen keterangan populasi daging ayam yang dihasilkan oleh Badan Pusat Statistik Nasional. Data yang digunakan dalam penelitian ini adalah data dari tahun 2009-2016 yang terdiri dari 34 provinsi. Variable yang digunakan (1) jumlah populasi dari tahun 2009-2016. Data akan diolah dengan melakukan clushtering dalam 3 clushter yaitu clusther tingkat populasi tinggi, clusther tingkat populasi sedang dan rendah. Centroid data untuk cluster tingkat populasi tinggi 4711403141, Centroid data untuk cluster tingkat populasi sedang 304240647, dan Centroid data untuk cluster tingkat populasi rendah 554200. Sehingga diperoleh penilaian berdasarkan indeks populasi daging ayam dengan 1 provinsi tingkat populasi tinggi yaitu Jawa Barat, 6 provinsi tingkat populasi sedang yaitu Sumatera Utara, Jawa Tengah, Jawa Timur, Banten, Kalimantan Selatan dan Kalimantan Timur, dan 27 provinsi lainnya termasuk tingkat populasi rendah. Hal ini dapat menjadi masukan kepada pemerintah, provinsi yang menjadi perhatian lebih pada populasi daging ayam berdasarkan cluster yang telah dilakukan
Application of C4.5 Algorithm in Improving English Skills in Students Septri Wanti Siahaan; Kristin Daya Rohani Sianipar; P.P.P.A.N.W Fikrul Ilmi R.H Zer; Dedy Hartama
Jurnal Informatika Universitas Pamulang Vol 5, No 3 (2020): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v5i3.5268

Abstract

In this world, many languages from other countries can be used as a communication tool. One of them is English. Students who has qualification must know that learning English is very much needed. Because nobody knows what will happen in the next few years. It could be one factor to obtain a position the next few years is our expertise in speaking English. English is a global language used by people to communicate with other people. On this occasion, researchers conducted research to determine what factors can improve students' ability to speak English. To complete this research, researchers resolve by applying the existing algorithm in data mining, namely C4.5 Algorithm. The result of this research can be concluded that the factors that influence to improve students ability in English are hearing from the environment.
ANALISIS ALGORITMA K-MEDOIDS CLUSTERING DALAM PENGELOMPOKAN PENYEBARAN COVID-19 DI INDONESIA Sukma Sindi; Weni Ratnasari Orktapia Ningse; Irma Agustika Sihombing; Fikrul Ilmi R.H.Zer; Dedy Hartama
(JurTI) Jurnal Teknologi Informasi Vol 4, No 1 (2020): JUNI 2020
Publisher : Universitas Asahan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36294/jurti.v4i1.1296

Abstract

Abstract - At the beginning of March Indonesia was entering the corona outbreak virus (COVID) Every day the case of Covid-19 distribution in Indonesia continued to increase. the community is issued to conduct social distance to cut the distribution of COVID-19 distribution distributed in various regions. In Indonesia, therefore, the data that has been accommodated is certainly a lot, from the data it can be seen patterns - selection patterns of distribution of COVID-19 distribution are based on test scores, This study uses the K-Medoids method so that the distribution patterns of COVID-19 distribution can be used for the community. K-Medoids is a method of grouping Analytical sections that aim to get a set of k-clusters among the data that most require an object in the collection of data. The results of the new COVID-19 research grouping show the community produced from various regions in Indonesia. Characteristics with a body temperature above 36.9 ◦ c and with fever and cough resolution supported by one of the characteristics of COVID-19 symptoms.Kata Kunci -    K-Medoids Algorithm, Clustering, Data Mining, COVID-19, Data GroupingAbstrak - Pada awal maret Indonesia sedang di landa masuknya wabah  virus corona (covid) Setiap hari kasus penyebaran covid-19 di indonesia terus meningkat.  masyarakat diminta untuk melakukan  social distancing guna mamutus rantai penyebaran covid-19 yang tersebar diberbagai wilayah.di Indonesia.  Oleh karena itu, data yang telah ditampung pastinya banyak sekali, dari data tersebut dapat dilihat pola – pola penentuan pengelompokan penyebaran covid-19 dilakukan berdasarkan nilai tes, Penelitian ini menggunakan metode K-Medoids agar dapat diketahui pola pemilihan penentuan pengelompokan penyebaran covid-19 bagi masyarakat. K-Medoids merupakan metode Analitis partisional clustering yang bertujuan untuk mendapatkan suatu set k-cluster di antara data yang paling mendekati suatu objek dalam pengelmpokan suatu data.. Hasil penelitian pengelompokan penyebaran covid-19 baru menunjukkan bahwa masyarakat yang berasal dari berbagai wilayah di Indonesia. Cirri-ciri dengan suhu badan  di atas 36,9◦c dan dengan disertai demam dan batuk berkelanjutan menunjukkan salah satu ciri-ciri  gejalah covid-19Kata Kunci - Algoritma K-Medoids, Clustering, Data Mining,  Covid-19, Pengelompokan Data
PENERAPAN ALGORITMA K-MEANS UNTUK MENGETAHUI TINGKAT TINDAK KEJAHATAN DAERAH PEMATANGSIANTAR Hotma Dame Tampubolon; Devi Gultom; Luvita Yolanda Hutabarat; Fikrul Ilmi R.H Zer; Dedy Hartama
(JurTI) Jurnal Teknologi Informasi Vol 4, No 1 (2020): JUNI 2020
Publisher : Universitas Asahan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (492.196 KB) | DOI: 10.36294/jurti.v4i1.1263

Abstract

Abstract - Crime is now a concern and concern for the community, especially those in the Pematangsiantar Region. Acts of crime that often occur are murder, theft, drugs, rape. With the rampant crime in Pematangsiantar City, it is necessary to group each region using the K-Means algorithm. The data source in this study is a collection of various documents of information Criminal Acts by Pematangsiantar Police Law. The data used in this study are data from 2019 consisting of 6 Districts. In this study, the K-Means algorithm is used to find out areas that have high crime rates and low crime rates areas.Keywords - Crime, K-Means, Clustering, Data Mining Abstrak - Kejahatan saat ini menjadi perhatian dan ke khawatiran bagi masyarakat terutama yang ada di Wilayah Pematangsiantar. Aksi kejahatan yang sering terjadi ialah pembunuhan, pencurian, narkoba, Pemerkosaan. Dengan maraknya kejahatan di Kota Pematangsiantar maka di perlukan pengelompokkan tiap daerah menggunakan algoritma K-Means. Sumber data pada penelitian ini merupakan kumpulan dari berbagai dokumen-dokumen keterangan Aksi Kriminalitas oleh Hukum Polres Pematangsiantar. Data yang digunakan pada penelitian ini adalah data dari tahun 2019 yang terdiri dari 6 Kecamatan. Dalam penelitian ini algoritma K-Means digunakan untuk mengetahui daerah yang memiliki tingkat kejahatan tinggi dan daerah tingkat kejahatan rendah.Kata Kunci -  Kejahatan, K-Means, Clustering, Data Mining
PENERAPAN ALGORITMA K-MEANS DALAM MENENTUKAN TINGKAT PENYEBARAN PANDEMI COVID-19 DI INDONESIA Nayuni Dwitri; Jose Andreas Tampubolon; Sandi Prayoga; Fikrul Ilmi R.H Zer; Dedy Hartama
(JurTI) Jurnal Teknologi Informasi Vol 4, No 1 (2020): JUNI 2020
Publisher : Universitas Asahan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (744.134 KB) | DOI: 10.36294/jurti.v4i1.1266

Abstract

Abstract  - COVID-19 is an infectious disease caused by acute coronavirus 2 (severe acute respiratory coronavirus 2 or SARS-CoV-2) respiratory syndrome. The even distribution of COVID-19 cases in all provinces in Indonesia is a fairly rapid spread and harms all fields. The vast territory of Indonesia allows the need for grouping sections by region in Indonesia. This grouping will produce center points for the spread of COVID cases - 19. K-Means is one of the Clustering algorithms that is used to divide data into several groups with a system partition. This algorithm accepts input in the form of data without class labels. Due to the global pandemic that occurred many parties sought to participate in overcoming. This research was conducted for application in the spread of the co-19 pandemic in Indonesia. In this study, the K-Means algorithm is used to determine the level of co-19 distribution in regions in Indonesia.Keywords - Algorithms, K-Means, Covid-19, The distribution, Pandemic Abstrak - COVID-19 adalah penyakit yang dapat menular jika adanya kontak antara penular dengan orang lain,dan  ditandai dengan  gejala pada bagian pernapasan disebut SARS-CoV-2. Penyebaran kasus COVID-19 yang merata di seluruh provinsi di Indonesia,merupakan penyebaran yang cukup cepat dan berdampak negatif pada seluruh bidang. Luasnya wilayah Indonesia memungkinkan diperlukannya pengelompokkan bagian bagian berdasarkan wilayah di Indonesia.Pengelompokkan ini akan menghasilkan titik – titik pusat penyebaran  kasus COVID -19. Salah satu algoritma Clustering adalah K-Means yang mengunakan beberapa kelompok untuk penempatan beberapa data dengan sistem partisi. Data-data tanpa label kelas diterima oleh Algoritma ini. Dikarenakan pandemi global yang terjadi banyak pihak berupaya ikut berperan serta dalam mengatasi. Penelitian ini dilakukan untuk penerapan dalam penyebaran pandemi covid-19 di Indonesia. Dalam penelitian ini mengunakan algoritma K-Means untuk menentukan bagaimana tingkat penyebaran covid-19 di daerah-daerah yang ada di Indonesia.Kata kunci - Algoritma, K-Means , COVID-19, Penyebaran, Pandemi  
PENERAPAN ALGORITMA K-MEANS DALAM MENENTUKAN TINGKAT KEPUASAN PEMBELAJARAN ONLINE PADA MASA PANDEMI COVID-19 Kristin Daya Rohani Sianipar; Septri Wanti Siahaan; Marina Siregar; Fikrul Ilmi R.H Zer; Dedy Hartama
(JurTI) Jurnal Teknologi Informasi Vol 4, No 1 (2020): JUNI 2020
Publisher : Universitas Asahan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (589.24 KB) | DOI: 10.36294/jurti.v4i1.1258

Abstract

Abstract - In this increasingly developed and advanced era, information technology is increasingly needed in human life. It can be seen from the current COVID-19 pandemic, information technology is increasingly needed in carrying out human activities at home. In the scope of learning, learning activities are shifted to online or online learning. Many students feel burdened with online learning. However, some students feel that the steps taken by the government are very appropriate to avoid stopping learning activities. This research was conducted for application in determining the level of online learning satisfaction in the COVID-19 pandemic. In this study the k-means algorithm is used to determine the level of online learning satisfaction that is applied to students.Keywords - Algorithm, K-Means, Level of Satisfaction, Online Learning, COVID-19 Pandemic Abstrak - Di era teknologi yang semakin mau dan berkembang ini, teknologi informasi menjadi lebih dibutuhkan dalam kehidupan manusia. Dapat dilihat dari adanya pandemi COVID-19 saat ini, teknologi informasi semakin dibutuhkan dalam melakukan aktivitas manusia di rumah. Pada ruang lingkup pembelajaran, aktivitas pembelajaran dialihkan menjadi melalui daring atau pembelajaran online. Banyak mahasiswa yang merasa terbebani dengan adanya pembelajaran online. Namun, ada juga mahasiswa yang merasa bahwa langkah yang dilakukan pemerintah ini sangat tepat untuk menghindari pemberhentian kegiatan pembelajaran. Penelitian ini dilakukan untuk penerapan dalam menentukan tingkat kepuasan pembelajaran online di tengah pandemi COVID-19. Dalam penelitian ini digunakan algoritma k-means untuk menentukan tingkat kepuasan pembelajaran online yang diterapkan kepada mahasiswa. Kata kunci - Algoritma, K-Means, Tingkat Kepuasan, Pembelajaran Online, Pandemi COVID-19
Penerapan Algoritma C4.5 Dalam 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
PETIR Vol 13 No 2 (2020): PETIR (Jurnal Pengkajian Dan Penerapan Teknik Informatika)
Publisher : Sekolah Tinggi Teknik - PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33322/petir.v13i2.1029

Abstract

In this world, many languages ​​from other countries can be used as a communication tool. One of them is English. Students who have education must know that learning English is very much needed. Because nobody knows what will happen in the years to come. It could be one factor to get a job the next few years is our expertise in speaking English. English is a global language used by people to communicate with other people. On this occasion, researchers conducted research to determine what factors can improve students' ability to speak English. To complete this research, researchers resolve by applying the existing algorithm in data mining, namely C4.5 Algorithm. The result of this research can be concluded that the factors that influence to improve students’s ability in English are hearing from the environment. This research has goals is to motivating student in improving their ability of English. Keywords: c4.5 algorithm, English, data mining, ability, improving
Automatic Car Control Prototype With Sound Application In Android Arduino Uno-Based: Automatic Car Control Prototype With Sound Application In Android Arduino Uno-Based Muhammad Ade Dharamawan Nasution; Dedy Hartama; Fitri Anggraini; Rafiqa Dewi; Eva Desiana
Jurnal Mantik Vol. 3 No. 4 (2020): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (704.407 KB)

Abstract

A car control device has been made with a voice application on an Android smartphone based on Arduino UNO. This tool uses HC-05 as a Bluetooth transmission media, Arduino Uno as the main controller and 2 DC motors as output. App Inventor is used to make a sound application on a smartphone, if the application receives a "forward" sound then both DC motors will advance. There are 5 voice commands that can be executed namely "forward", "backward", "right", "left" and "stop". From the results of trials that have been determined that this tool can control the car by giving voice commands that have been configured with a microcontroller with a considerable distance 3-5 meters.
OPTIMASI METODE AHP DALAM MENENTUKAN MEDIA PROMOSI BAGI MAHASISWA BARU PADA STIKOM TUNAS BANGSA PEMATANGSIANTAR Riski Sundari; Muhammad Safii; Dedy Hartama; Poningsih Poningsih
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 1, No 1 (2017): Intelligence of Cognitive Think and Ability in Virtual Reality
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v1i1.516

Abstract

Promotion is a very important issue for every academic because it involves the continuation of the academic development. STIKOM Tunas Bangsa is one of PTS in North Sumatera which is engaged in computer science. The process of admission of new students has the same procedure that is doing promotion to various regions in order to obtain human resources who will be a student of the college. To conduct promotional activities, STIKOM Tunas Bangsa through the marketing team must consider many factors, including promotional media used to obtain new students. From the above problems the author wants to create a decision support system that can provide solutions to the problem. One of the methods DSS that can be used is the method of Analytical Hierarchy Process (AHP). AHP is a decision support model developed by Thomas L. Saaty. This decision support model will describe the problem of multy factor or complex multi criteria into a hierarchy, according to the time (1993) the hierarchy is defined as a representation of a complex problem in a multi-level structure where the first level is the goal, and followed by the factor level , Criteria, sub criteria, and so on down to the last level of the alternative. With a hierarchy, a complex problem can be broken down into groups that are then organized into a hierarchical form so that the problem will seem more structured and systematic. From the results of this study is expected to assist the management, especially the marketing team in providing the best media promotion solutions for new students at STIKOM Tunas Bangsa Pematangsiantar.
Co-Authors Agus Perdana Windarto Devi Gultom Dewi, Rafiqa Eva Desiana Fikrul Ilmi R.H Zer Fikrul Ilmi R.H.Zer Fitri Anggraini Hotma Dame Tampubolon Iin Parlina Irma Agustika Sihombing Jose Andreas Tampubolon Kristin Daya Rohani Sianipar Luvita Yolanda Hutabarat Marina Siregar Mhd Gading Sadewo Muhammad Ade Dharamawan Nasution Muhammad Safii Nayuni Dwitri P.P.P.A.N.W Fikrul Ilmi R.H Zer Poningsih Poningsih Riski Sundari Sandi Prayoga Septri Wanti Siahaan Sukma Sindi Wanto, Anjar Weni Ratnasari Orktapia Ningse