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All Journal Dinamik Sinkron : Jurnal dan Penelitian Teknik Informatika Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Tekinkom (Teknik Informasi dan Komputer) INFOKUM Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Media Informatika TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Jurnal Ilmiah Multidisiplin Indonesia INOVTEK Polbeng - Seri Informatika
Indra, Evta
Universitas Prima Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Physics

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PREDIKSI WATER QUALITY INDEX (WQI) MENGGUNAKAN ALGORITMA REGRESSI DENGAN HYPER-PARAMETER TUNING Radhi, Muhammad; Amalia, Amalia; Sinurat, Stiven Hamonangan; Sitompul, Daniel Ryan Hamonangan; Indra, Evta
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 5 No. 1 (2021): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v5i1.2492

Abstract

Air merupakan salah satu sumber daya alam esensial untuk kelangsungan hidup seluruh makhluk hidup di dunia ini. Kita memerlukan air untuk kebutuhan kita sehari-hari, begitu juga dengan tanaman dan hewan yang memerlukan air untuk kelangsungan hidupnya. Indeks Kualitas Air (Water Quality Index/WQI) merupakan satuan untuk mengetahui apakah air dapat dinyatakan layak minum (Potable) atau tidak. Pada penelitian ini, untuk membuat prediksi nilai WQI lebih akurat dan memiliki tingkat akurasi model yang lebih tinggi, dirancang sebuah algoritma regresi yang kemudian akan dikonfigurasi kembali dengan tuning algoritma. Model machine learning yang telah dibuat memiliki nilai yang berbeda, namun pada penelitian ini telah ditentukan bahwasanya model Linear Regression yang dipakai sebagai model utama karena memiliki nilai R2 lebih tinggi (0.9965 / 96,5%). Sesuai dengan hasil plotting dari Linear Regression, data diprediksi dengan baik dan persebaran data masih berdekatan dengan prediksi model (robust).
Comparison of Classification Algorithm in Predicting Stroke Disease Hutabarat, Fenna Kemala; Sitompul, Daniel Ryan Hamonangan; Sinurat, Stiven Hamonangan; Situmorang, Andreas; Ruben, Ruben; Ziegel, Dennis Jusuf; Indra, Evta
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 1 (2022): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2714

Abstract

ABSTRAK- To prevent stroke, we need a way to predict whether someone has had a stroke through medical parameters. With the influence of technology in the medical world, stroke can be predicted using the Data Science method, which starts with Data Acquisition, Data Cleaning, Exploratory Data Analysis, Preprocessing, and the last stage is Model Building. Based on the model that has been made, it is concluded that the algorithm with the best performance, in this case, is XGBoost with a precision value of 0.9, a recall value of 0.95, an f1 value of 0.92, and a ROC-AUC value of 0.978 after receiving five folds of cross-validation. With these results, the model created can be used to make predictions in real-time. Kata kunci : Machine Learning, Logistic Regression, Random Forest, XGBoost, Stroke
Comparative Analysis of Indonesian Text Mining News Online Classification Using the K-Nearest Neighbor and Random Forest Algorithm Sihombing, Oloan; Sitorus, Sarah Tri Yosepha; Indra, Evta; Sinurat, Stiven Hamonangan; Juanta, Palma
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 1 (2022): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2824

Abstract

The rapid development of internet technology today makes many news media grow pretty rapidly. Newspaper companies have utilized internet technology to spread the latest news online through online mass media. Hundreds of thousands of stories are written and published daily on online-based Indonesian news portals, making it difficult for readers to find the news topics they want to read. In making it easier for readers to find the news they are looking for, news needs to be classified according to its respective categories, such as education, current news, finance, and sports. So to classify categories, a text classification method is needed or often called Text Mining. Text mining is a data mining classification technique for processing text using a computer to produce helpful text analysis. In this study, a comparison of 2 methods for developing texts was carried out to get accuracy above 80%.
THYROID DISEASE CLASSIFICATION ANALYSIS USING XGBOOST MULTICLASS panjaitan, haris samuel pranada; Gulo, Agustinus; Alfi, Ahmad Haikal; Harmaja, Okta Jaya; Indra, Evta
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 1 (2022): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2831

Abstract

ABSTRAK- Sickness is an unusual condition of the body or mind that causes discomfort, malfunction, or suffering to the sick person. One disorder that occurs due to a lack of health concerns is thyroid disease. The thyroid is a butterfly-shaped endocrine gland near the neck's bottom. The diagnosis of thyroid disease is complicated because the symptoms of thyroid disease can fluctuate based on the rise and fall of thyroid hormones, which increase the utilization of oxygen by the body's cells. In this case, a thyroid examination by a doctor and proper interpretation of clinical data is required to identify thyroid disease. However, the limitations of a doctor due to age and time constraints lead to a lack of interpretation of patient clinical data. Therefore, a study was conducted on the analysis of thyroid disease classification to simplify and speed up the process of diagnosing thyroid disease using the Xgboost Multiclass method, which is expected to get an accuracy value above 90%. Keywords: Classification, Thyroid, Xgboost Multiclass, Machine Learning
COMPARISON OF CLASSIFICATION ALGORITHM IN CLASSIFYING AIRLINE PASSENGER SATISFACTION Indra, Evta; Suwanto, Jacky; Sitompul, Daniel Ryan Hamonangan; Sinurat, Stiven Hamonangan; Situmorang, Andreas; Ruben, Ruben; Ziegel, Dennis Jusuf
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 1 (2022): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2848

Abstract

In order to revive the airline industry, which is being hit by the current recession, it is essential to restore passenger confidence in airlines by improving the services provided by airlines. With the influence of technology in all industrial fields, airlines can now use Machine Learning to find the essential points that can make passengers feel satisfied with airline services and classify passenger satisfaction. This study presents the making of Machine Learning models starting from Data Acquisition, Data Cleaning, Exploratory Data Analysis, Preprocessing, and Model Building. It is concluded that Random Forest is the best algorithm used in this case study, with an F1 accuracy score of 89.4, ROC-AUC score of 0.90, and a shorter modeling period than other algorithms used in this study.
Laptop Price Prediction with Machine Learning Using Regression Algorithm Siburian, Astri Dahlia; Sitompul, Daniel Ryan Hamonangan; Sinurat, Stiven Hamonangan; Situmorang, Andreas; Ruben, Ruben; Ziegel, Dennis Jusuf; Indra, Evta
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 1 (2022): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2850

Abstract

Since the COVID-19 pandemic, many activities are now carried out in a Work From Home (WFH) manner. According to data from the Central Statistics Agency (BPS) of East Java, in 2021, large and medium-sized enterprises (UMB) who choose to work WFH partially are 32.37%, and overall WFH is 2.24% (BPS East Java, 2021 ). With this percentage of 32.37%, many people need a work device (in this case, a laptop) that can boost their productivity during WFH. WFH players must have laptops with specifications that match their needs to encourage productivity. To prevent buying laptops at overpriced prices, a way to predict laptop prices is needed based on the specified specifications. This study presents a Machine Learning model from data acquisition (Data Acquisition), Data Cleaning, and Feature Engineering for the Pre-Processing, Exploratory Data Analysis stages to modeling based on regression algorithms. After the model is made, the highest accuracy result is 92.77%, namely the XGBoost algorithm. With this high accuracy value, the model created can predict laptop prices with a minimum accuracy above 80%.
THE USE OF DATA AUGMENTATION AND EXPLORATORY DATA ANALYSIS IN ENHANCING IMAGE FEATURES ON APPLE LEAF DISEASE DATASET Rifaldo, Rifaldo; Sitompul, Daniel Ryan Hamonangan; Sinurat, Stiven Hamonangan; Situmorang, Andreas; Rahmad, Julfikar; Ziegel, Dennis Jusuf; Indra, Evta
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 2 (2023): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i2.3438

Abstract

Apples are an essential commodity produced in Batu City, Malang. In 2017, Batu City, Malang, produced 19.1 tons of apples, while in 2018, Batu City, Malang, produced 15.9 tons of apples. It can be concluded that the decline in the number of apple harvests in Batu City, Malang. With the influence of technology in agriculture, the influence of technology can be used to detect diseases on leaves to overcome the decrease in the number of harvests. With the Image Augmentation method used in this study, the existing dataset can have 6x more features. So that the healthy category, which previously had 516 image features, now has 3096 image features, the scab category, which previously had 592 image features, now has 3552 image features and the rust category, which previously had 622 image features, now has 3732 image features. With a dataset with 3000 image features, the model to be made can have a higher accuracy value. The model can be said to be sturdy/sturdy/good, or the model to be made can carry out the classification process with a good level of accuracy.
ANALYSIS OF STUDENT SATISFACTION WITH STUDENT MANAGEMENT SERVICES IN THE INFORMATION SYSTEMS STUDY PROGRAM AT PRIMA INDONESIA UNIVERSITY USING THE SERVICE QUALITY (SERVQUAL) METHOD Wijaya, Malvin Luckianto; Sitompul, Daniel Ryan Hamonangan; Sinurat, Stiven Hamonangan; Rahmad, Julfikar; Fahmi, Mohammad Irfan; Indra, Evta
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 2 (2023): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i2.3446

Abstract

The purpose of this research is to analyze the level of student satisfaction with student management services in the Information Systems Study Program at Prima Indonesia University. The method used in this study is the Service Quality method to measure the level of student satisfaction. The results of the research show that the level of student satisfaction with student management services is 4.28 and the Servqual method has good quality in measuring student satisfaction.
FUZZY LOGIC FOR OPTIMIZING ROOM SALES: SUGENO METHOD AND MAPE EVALUATION Suhamdani, Dadan; Christian, Daniel; Bangun, Frans Aditya; Rifai, Ahmad; Indra, Evta
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 1 (2023): 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.v7i1.4075

Abstract

The Indonesian hospitality industry has grown rapidly over the past decades and is one of the most important sectors of the national economy. The focus is on determining the number of rooms sold using the Sugeno method's fuzzy logic. This study optimizes room sales by developing a fuzzy logic-based system that can effectively determine the number of rooms sold considering availability, best available rates, and revenue target. The Sugeno method is a type of fuzzy inference system that determines the relationship between input variables (room availability, best available rate, revenue target) and output variables (number of rooms sold). Modeled by using linguistic variables and fuzzy rules, the Sugeno method can provide a quantitative output based on specified input conditions. To evaluate the accuracy of the proposed fuzzy logic model, the mean absolute percentage error (MAPE) is used as a performance measure. Target data 175,000,000 to 245,000,000, BAR standard room 225,000 to 335,000, BAR superior room 285,000 to 425,000, available standard room 68 rooms/day, superior room 10 rooms/day, model accuracy measurement result is 1,80% very accurate interpreted. As such, the proposed system is useful for decision-making related to optimizing room sales in the hospitality industry.
SENTIMENT ANALYSIS OF MYPERTAMINA APPLICATION USING SUPPORT VECTOR MACHINE AND NAÏVE BAYES ALGORITHMS Simbolon, Ongki Sopie; Manullang, Murni Esterlita; Alvarez, Stevin; Brutu, Lolo Frans M.; Indra, Evta
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 1 (2023): 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.v7i1.4078

Abstract

In line with the needs of the community and the progress of the times in the advanced field of fintech, cash payments are currently considered insecure as well as ineffective and efficient. To run a non-cash or cashless transaction program currently run by the government, PT. Pertamina invites the public to use E-Payment from the My Pertamina application in collaboration with LinkAja. In this study, the sentiments of MyPertamina application users will be analyzed based on reviews on the Google Play Store. Review data will be analyzed to determine whether the review has positive, negative, or neutral sentiments. The data analysis stage is text preprocessing to change uppercase to lowercase, clearing text, separating text, taking important words, changing essential words, and labeling data into positive, negative, and neutral classes. As well as the classification and evaluation of results. This study used the Support Vector Machine (SVM) and Naïve Bayes classification methods. To evaluate the results, the confusion matrix was used to test the accuracy, precision, recall, and F1 score value. The classification results obtained the highest accuracy value for the Support Vector Machine (SVM) method, which had accuracy (68.50%), precision (70.00%), recall (69.70%), and F1 score (68.46%). Meanwhile, the Naïve Bayes method has performance with accuracy (63.00%), precision (63.90%), recall (61.34%), and F1 score (59.55%).
Co-Authors ., Calvin Abellista, Tivanez Ballerina Ahmad Rifai Akbari, Deni Adha Alfi, Ahmad Haikal Alifah, Lutfi Aulia Alvarez, Stevin Amalia Amalia Aminatunnisa, Siti Amir Saleh ANITA . Bangun, Dea Monica Bangun, Frans Aditya Banjarnahor, Jepri Barus, Daniel Haganta Brutu, Lolo Frans M. Butarbutar, Serly Yunarti Buulolo, Deniarwinus Candra, Kevin daniel christian Delima Sitanggang, Delima Dina Pratiwi, Dina Dwi Rizky, Atikah Edison, Rizki Edmi Fahmi, Mohammad Irfan Fando, Al Farrona, Rio Fidelis, Rio Giawa, Well Friend Ginting, Nessa Sanjaya Ginting, Ricci Kincahar Bastoto Kevin Gulo, Agustinus Gultom, Yeni Gurusinga, Alta Harahap, Charles Bronson Hutabarat, Fenna Kemala Hutabarat, Lerry Yos Santa Angelina Hutasoit, Leo Nardo Hutauruk, Jesika Avonia Juanta, Palma Juliandra, Vella Karim, Anggie Monica Keliat, Ribka Amelia Yunita Kumar, Sharen Loi, Mentari Hati Lowell, Alvis Lowell, Audric Lumbanraja, Lamtiur Rondang Wulan Maharja, Okta Jaya Manullang, Murni Esterlita Mariyanti, Eka Marpaung, Aldo Andy Yoseph Tama Matondang, Enjelika Mawaddah Harahap, Mawaddah MAYANTI, NUR Meizar, Abdul Muhammad Farhan Muhammad Yasir Muhardi Saputra Napitupuluh, Christian Deniro Nasution, Adli Abdillah Nasution, Syafrani Putri NK Nababan, Marlince Okta Jaya Harmaja Oloan Sihombing, Oloan Pakpahan, Ferdinand Linggo Panjaitan, Ezra Christina Septiana panjaitan, haris samuel pranada Piay, Clara Stephanie Bernadeth Pratama, Febryan Purba, Salda Sari Rahil, Rafif Rahmad, Julfikar Reinaldo, Erick Rifaldo, Rifaldo Ruben Ruben, Ruben Saragih, Septua Fujima Sembiring, Diarnia Mega Selfia Sembiring, Joni Satrio Sembiring, Yudha Brema Agriva Sianturi, Santo Sanro Siburian, Astri Dahlia Silaban, Herlan Simajuntak, Yusuf Natanael Simamora, Wanda Pratama Putra Simangunsong, Sarah Simanjuntak, Mega Herlin Simarmarta, Brando Benedictus Simbolon, Ongki Sopie Sinaga, Putri tua Sinurat, Stiven Hamonangan Siregar, Frissy Siregar, Reinhrad Shodani Siringo Ringo, Jaka Tomi Ronaldo Sitanggang, Audina L Sitompul, Chris Samuel Sitompul, Daniel Ryan Hamonangan Sitorus, Sarah Tri Yosepha Situkkir, Miando Mangara Situmorang, Andreas Solly Aryza Suhamdani, Dadan Suwanto, Jacky Suyanto, Jao Han Tampubolon, Irfan Saputra Tarigan, Nina Veronika Tarigan, Sri Wahyuni VERONICA VERONICA Vicraj, Vicraj Wijaya, Malvin Luckianto Wiranto, David Wiratama, Westlie Wirhan Fahrozi, Wirhan Yonata Laia Ziegel, Dennis Jusuf