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All Journal Jurnal TIMES Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Sisfokom (Sistem Informasi dan Komputer) Jiko (Jurnal Informatika dan komputer) JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Informatika Kaputama (JIK) Jurnal Sistem Informasi Kaputama (JSIK) Jurnal Mantik Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Sains dan Teknologi Jurnal Tekinkom (Teknik Informasi dan Komputer) INFOKUM Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Journal of Computer Networks, Architecture and High Performance Computing Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) JURNAL MITRA PRIMA Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam International Journal of Information System & Innovative Technology TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer International Journal of Information System and Innovative Technology INOVTEK Polbeng - Seri Informatika
Delima Sitanggang, Delima
Universitas Prima Indonesia
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Sentiment Analysis of Digital Television Migration on Twitter Using Naïve Bayes Multinomial Comparison, Support Vector Machines, and Logistic Regression Algorithms Dahlian, Ryo Benhard; Sitanggang, Delima
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 2 (2023): JULI
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i2.1668

Abstract

The Ministry of Communication and Information Technology (KEMENKOMINFO) has announced to the publics in Indonesia regarding the termination of analog television broadcasts or called analog switch-off, which requires the public to migrate from analog television to digital television. Regarding the process of stopping analog broadcasts this raises pros and cons by the people in Indonesia. Many people give their respective opinions through social media, especially on Twitter. A collection of pros and cons data from the public can be collected and used as research of sentiment analysis. This research will focus on comparing three classification algorithms, which is called Multinomial Naïve Bayes, Support Vector Machines, and Logistic Regression using the same dataset and the same method called Lexicon Based. The results showed that the highest accuracy is Support Vector Machines with the accuracy is 94.00%, Logistic Regression with the accuracy is 90.00%, and Multinomial Naïve Bayes with the accuracy is 88.00%.
EEG Signal Classification using K-Nearest Neighbor Method to Measure Impulsivity Level Ginting, Arico Sempana; Simanjuntak, Ruth Marsaulina; Lumbantoruan, Nurima; Sitanggang, Delima
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2154

Abstract

Impulsivity is the tendency to act without considering consequences or without careful planning. It involves a quick response to a stimulus without sufficient consideration of the consequences. Impulsivity needs to be measured and detected because it has a significant impact on various aspects of a person's life. The factors that influence the level of impulsivity include social environment, stress level, mental health, and genetic factors. Impulsivity can be divided into multiple components, such as reduced sensitivity to unfavorable behavioral outcomes, a disregard for long-term implications, and quick and spontaneous responses to stimuli. Electroencephalogram (EEG) studies can identify specific brain wave patterns such as, Alpha, Betha, Theta, and Gamma waves everything based on an individual brain's level of impulsivity. Signals from the brain are processed to extract specific features that reflect the user's intentions. EEG records brain activity without surgery, and this information is used for the diagnosis, monitoring, and treatment of neurological diseases, as well as scientific research on the brain and mind. K-Nearest Neighbor (KNN) is a classification algorithm that functions by utilizing several K nearest data values (its neighbors) as a reference to determine the class of new data. The K-Nearest Neighbors (KNN) algorithm is used for classification, clustering, and pattern recognition in EEG data where clustering is in 4 classifications (Impulsive, Not Impulsive, Potentially Impulsive, and Very Potentially Impulsive). This classification model shows high accuracy (Training Data: 94.7%, Testing: 91.3%, and Validation Data: 91.8%). This research shows that the KNN algorithm is effective for assessing the degree of impulsivity.
APLIKASI PERHITUNGAN ANGKA KREDIT JABATAN FUNGSIONAL DOSEN BERBASIS WEB MEGGUNAKAN MODEL WATERFALL Turnip, Mardi; Bolon, Debby Novriyanti Br Tp.; Nababan, Marlince N.K; Sitanggang, Delima
Jurnal Sistem Informasi Kaputama (JSIK) Vol. 2 No. 1 (2018): Volume 2, Nomor 1, Januari 2018
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jsik.v2i1.791

Abstract

Universitas Prima Indonesia merupakan salah satu perguruan tinggi swasta yang masih menggunakan sistem komputerisasi namun masih terbatas melakukan pengolahan data. Proses ini memungkinkan hilangnya data atau arsip yang rusak dan dapat membuat kesalahan terus-menerus. Hal ini diperlukan untuk menciptakan aplikasi atau sistem informasi yang dapat mempermudah proses pengajuan jabatan pangkat dan jabatan fungsional agar terhindar dari kesalahan yang terus menerus. Untuk mengatasi permasalahan di atas penulis tertarik untuk membuat Aplikasi Perhitungan Angkat Kredit Jabatan Fungsional Dosen Berbasis Web di Universitas Prima Indonesia yang diharapkan dapat mengurangi kesalahan umum, proses yang ada dan dapat mempermudah pengolahan data dosen serta informasi yang berkaitan dengan promosi dan posisi dosen.
Analysis of Air Quality Measuring Device Using Internet of Things-Based MQ-135 Sensor Sitanggang, Delima; Sitompul, Chris Samuel; Suyanto, Jao Han; Kumar, Sharen; Indra, Evta
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11618

Abstract

Air is a gas that is indispensable for the survival of living beings. As the times progress, the air we breathe is increasingly not good for the health of living beings. In most situations, humans cannot tell the difference between good and bad air conditions. The purpose of this research is to design a tool that can monitor air quality in many places using an Internet of Things-based concept, the MQ-135 gas sensor and display it on a 16x2 LCD and Blynk application. This study uses a direct test method to identify gases around the MQ-135 sensor with the NodeMCU ESP 8266 as a controller. Air quality is divided into 5 categories, which consists of good, average, unhealthy, very unhealthy, and dangerous. After the air quality value is displayed on the 16x2 LCD screen, the user can monitor the air quality remotely using the blynk application on the smartphone. It can be concluded that the design of this tool can detect air quality in classrooms, vehicle exhaust fumes, gas lighters, house rooms, and burned paper. If the air quality is bad, the buzzer will release the sound to notify that the air quality is poor according to the index of air quality.
CLASSIFICATION OF ELECTROCARDIOGRAM (ECG) WAVES OF HEART DISEASE USING THE XGBOOST METODE METHOD Butarbutar, Serly Yunarti; Napitupuluh, Christian Deniro; Ginting, Nessa Sanjaya; Indra, Evta; Sitanggang, Delima
INFOKUM Vol. 10 No. 02 (2022): Juni, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (502.556 KB)

Abstract

CLASSIFICATION OF ELECTROCARDIOGRAM (ECG) WAVES OF HEART DISEASE USING THE XGBOOST METODE METHOD
APPLICATION OF DATA MINING USING THE RANDOM FOREST METHOD TO PREDICT HEART DISEASE Felix, Felix; Sitanggang, Delima; Laia, Yonata; -, Amalia; Radhi, Muhammad; Barus, Ertina Sabarita
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 2 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i2.4801

Abstract

A heart attack is when fatty deposits block the arteries. This causes symptoms such as shortness of breath and chest pain. In addition, obstructed blood flow to the heart can cause damage to the heart muscle. Heart attacks are still the highest cause of death in Indonesia to date. The problem today is that it is tough to predict and identify heart disease. The appropriate method needed to predict heart disease is the Random Forest method. This research aims to calculate the level of accuracy in predicting heart attacks. Based on research and data processing carried out by previous study by comparing two K-Neighbor algorithms, which produced an accuracy value of 83% and the Logistic Regression algorithm produced an accuracy value of 88% and it was found that the Random Forest algorithm had an accuracy of 86.88%. Thus, other algorithms are better at predicting heart attacks than the Random Forest algorithm. Keywords: Heart Attack, Random Forest, Prediction.
THE IMPACT OF AN ANIMATED VIDEO INQUIRY TRAINING MODEL ON JUNIOR HIGH SCHOOL SCIENCE STUDENTS’ LEARNING OUTCOMES Sitorus, Angelina Monica; Tampubolon, Johanes Joys Ronaldo; Juanta, Palma; Sitanggang, Delima
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

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

Abstract

The purpose of this study was to determine how the use of the questioning learning model with animated videos at Dr. Wahidin Sudirohusodo Private Junior High School in Medan, North Sumatra, impacts student learning outcomes on science materials. This study used question training, with two groups used for pretest and posttest. The study involved all students of Dr. Wahidin Sudirohusodo Private Junior High School in Medan, North Sumatra. The study involved grade VIII students spread across several parallel classes. The sample was randomly selected. There were 66 students in class VIII-1 and VIII-3. Class VIII-1 was the experimental class with 34 students, and class VIII-3 was the control class with 32 students. The results of the research show that the Inquiry Training Learning Model using animated video media on the science learning outcomes of students in the experimental class shows a good attitude compared to the control class, as seen from the increase in scores as evidenced by the experimental class’s mean score which is higher compared to the control class with the mean score for the post-test for the experimental class was 65.7647 and for the control class 42.6250.
APPLICATION OF NAIVE BAYES ALGORITHM FOR SALES ANALYSIS AT ERIGO STORE Sitanggang, Maria Natalenta; Ambarita, Rivandu; Marpaung, Cantika; Sitanggang, Delima
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5404

Abstract

The purpose of this study is to research and compare the accuracy of the previous research algorithm, namely the KNN algorithm with the Naive Bayes algorithm, for the evaluation of Erigo Store sales. Given the increasingly fierce market competition, it is very necessary to formulate a marketing strategy to analyze and predict products using data mining processing methods. Data mining is the introduction of patterns, machine learning techniques, statistics, and visualization techniques that aim to provide information to make better decisions and improve prediction accuracy through the process of analyzing data based on the Knowledge Discovery in Database (KDD) procedure. The research dataset was taken from shopee Toko Erigo e-commerce sales data using web scraping techniques, starting from January 2021 to June 2023 consisting of 5 categories of Erigo Store products, namely Shirts, T-Shirt, Outwear, Jacket and Pants. The overall accuracy of the previous research product using the KNN algorithm was 83.62% while the study using the application of the Naive Bayes algorithm for sales analysis in Erigo stores achieved an accuracy of 98.3% by using Matlab to analyze the data. The accuracy of the T-shirt category reached 98.6%, the shirt category reached 98.4%, the pants category reached 98.1%, the outwear category reached 98.7% and the accuracy of the jacket category reached 97.6%.
PERBANDINGAN ALGORITMA C5.0 DAN K-MEANS CLUSTERING UNTUK MEMPREDIKSI KEPUASAN MAHASISWA TERHADAP KINERJA DOSEN UNIVERSITAS PRIMA INDONESIA Jefri Syah Putra Laoli; Sadarman Zebua; Novanius Lahagu; Delima Sitanggang; Evta Indra
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 2 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i2.938

Abstract

Data mining is an attempt to dig up valuable and useful information on very large databases. Data mining is an operation that uses certain techniques or methods to look for a pattern or different form in a selected data. The technique used in this study is using data mining with the C5.0 and K-Means methods. The purpose of this study was to determine student satisfaction by using and comparing the accuracy of the C5.0 and K-Means clustering algorithms in predicting student satisfaction on lecturer performance at Universita Prima Indonesia, Faculty of Science and Technology. The results of research using the C5.0 Algorithm method where the accuracy value obtained is 90.90% (Very Satisfied) while the accuracy value is 9.10% (Not Satisfied). The K-Means Clustering method gives quite good results in classifying data, more than 75% of respondents feel (Very Satisfied) while less than 25% feel (Not Satisfied) from the teaching given by lecturers at Universitas Prima Indonesia.
ANALISIS DALAM MENENTUKAN STOK BARANG MASUK DARI SUPPLIER PADA BLIBLI.COM MEDAN MENGGUNAKAN METODE ASSCOATION RULE DENGAN ALGORITMA APRIORI Delima Sitanggang; Oktoberto Perangin-angin; Esther Mayorita Nababan; Meri Natasia Napitupulu; Evta Indra
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 2 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i2.936

Abstract

The divisions that support the service production business continue to be developed, so as to provide more benefits for the organizers. One part that continues to be researched is the determination of suppliers of goods for production companies, because a good supplier will not only maximize cooperative relations, but also supply quality raw materials, as well as provide convenience in the process of importing goods. in the previous calculations the minimum support value that had been determined was 3.5%, so the researcher formed 1 itemset where the support value met the percentage in the category (accessories 5.8%), (cigarette 11.4%), (cooking ingredients 23.1%), (electronic 30.9%), (food 28.1%), (gagdet,46.6%), (healthy&beauty 12.4%), and y(kids&baby 4.2%) , in the formation of 2 itemsets where those containing A are combined with those containing item B which fulfill the percentages on (electronics, food 19.3%) , (gadgets, cooking ingredients 23.9%), (gagdet, electronics 37.6%) , and (gagdet, food 19.3%) in the formation of 3 itemsets with support (A,B,C) there is no item value that meets the minimum support value and for the minimum confidence value in this study 40% in the establishment of a rule or association of 2 itemsets that meet confidence (cigarettes, kids baby 52.6%), and (kids babies, accessories 47.6%), for the establishment of a 3 itemset association rule that meets the minimum confidence value (kids babies, electronics, accessories 47.6%), (kids baby, fozen food, accessories 47.6%), (food, gadgets, kids baby 95.2%), (kids baby, fozen food, cooking ingredients 47.6%).
Co-Authors -, Amalia ., Calvin ., Efendy ., Kelvin Abdi Dharma Achmad Ridwan, Achmad Ade Sahputra Nababan Agung Prabowo Agustinus Lumban Raja Albert Sagala, Albert Alvina, Jesslyn Ambarita, Rivandu Amir Mahmud Husein, Mawaddah Harahap, Amir Angie, Vicky Anita Anita Anita Christine Sembiring Ayu Rahayu Sagala Ayu Rosalya Sagala Barus, Ertina Sabarita Bolon, Debby Novriyanti Br Tp. Butarbutar, Serly Yunarti Cloudia Stevani Saragih Sumbayak Cristian Andika Tarigan Dahlian, Ryo Benhard David David Debby Novriyanti Br Tp.Bolon Djuli, Zachary Esther Mayorita Nababan Etriska Prananta S. Evta Indra Evta Indra Faijriah Nazla Sahira Felix Felix Ginting, Arico Sempana Ginting, Nessa Sanjaya Ginting, Riski Titian Grace Aloina Greace HS, Christnatalis Hutahaean, Rani Hutasoit, Feliks Daniel Iboy Erwin Saragih, Rijois Immanuel Sinaga, Ferdy Indra, Evta Indren, Indren Intan Susanti Simarmata Jefri Syah Putra Laoli Jorgi L.Tobing, Stefanus Juan Juanta, Palma Kumar, Sharen Lee, Brandon Lidya Silalahi Lumbantoruan, Nurima Manao, Sonatafati Manday, Dhanny Rukmana Mardi Turnip, Mardi Maria Yostin Br Tarigan Marlince N.K Nababan Marpaung, Aldo Andy Yoseph Tama Marpaung, Cantika Matthew Oullanley Lee Meri Natasia Napitupulu Mita Aprila Silpa Simanjuntak Muhammand Ridho Muliadi Marianus Sirait Musa Andrew Loyd Sitanggang Nababan, Marlince N.K Nainggolan, Winner Parluhutan Nanchy Adeliana Br S. Muham Napitupuluh, Christian Deniro Niken Sihombing Nina Purnasari Nova Riani Fransiska Novanius Lahagu Oktarino, Ade Oktoberto Perangin-angin Pamungkas, William Aldo Perangin Angin, Despaleri Perangin-angin, Despaleri Pungki Laurensius Ritonga Putra, Muhammad Amsar Rijois I. E. Saragih Rizal, Reyhan Achmad Sadarman Zebua Saljuna Hayu Rangkuti Sanjaya, Federico Saragi, Yosua Morales Saragih, Rini Hartati Sarah Simangunsong Saut Parsaoran Tamba sherly sherly Siahaan, Edivan Wasington Siahaan, Eric Simon Giovanni Sihotang, Putri Anasia Simangunsong, lamria Simanjuntak, Ester Farida Simanjuntak, Mega Herlin Simanjuntak, Ruth Marsaulina Simarmarta, Brando Benedictus Sinaga, Jasmin William Natanael Sion Putri Zalukhu Siregar, Saut Dohot Sitanggang, Maria Natalenta Siti Aisyah Siti Aisyah Sitompul, Chris Samuel Sitorus, Angelina Monica Situkkir, Miando Mangara Solly Aryza Sri Wahyu Tarigan Sri Wahyuni Tarigan Sumita Wardani Sundah, Geertruida Frederika Suyanto, Jao Han Tampubolon, Irfan Saputra Tampubolon, Johanes Joys Ronaldo Tampubolon, Tasya Rouli Christy Tarigan, Julio Putra Tarigan, Nina Veronika Tarigan, Sri Wahyuni Tifanny, Tifanny Togar Timoteus Gultom Wijaya, Bryan Wilbert Solo, Eddrick Winarti Pasaribu Yennimar Yennimar, Yennimar Yoga Tri Nugraha Yonata Laia Yumna, Farhan