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All Journal International Journal of Evaluation and Research in Education (IJERE) Jurnal Kependidikan: Penelitian Inovasi Pembelajaran Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science Cyberspace: Jurnal Pendidikan Teknologi Informasi INOVTEK Polbeng - Seri Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JURNAL PENDIDIKAN TAMBUSAI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Antivirus : Jurnal Ilmiah Teknik Informatika Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Mnemonic INFORMASI (Jurnal Informatika dan Sistem Informasi) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Abdimasku : Jurnal Pengabdian Masyarakat Aiti: Jurnal Teknologi Informasi Jurnal Teknologi Informasi dan Komunikasi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Journal of Business and Audit Information System (JBASE) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Decode: Jurnal Pendidikan Teknologi Informasi Jurnal Minfo Polgan (JMP) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Pengabdian Papua IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Eduvest - Journal of Universal Studies SmartComp Jurnal Pendidikan Teknologi Informasi (JUKANTI) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Evangs Mailoa
Universitas Kristen Satya Wacana
Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Social Sciences Other

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PERBANDINGAN IMPLEMENTASI METODE SMOTE PADA ALGORITMA SUPPORT VECTOR MACHINE (SVM) DALAM ANALISIS SENTIMEN OPINI MASYARAKAT TENTANG MIXUE Dewi, Teresia Ardika; Mailoa, Evangs
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 3 (2023): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i3.289

Abstract

In December 2022, the development of a franchise for an ice cream and tea outlet from China named Mixue became the talk of the Indonesian people, especially on social media Twitter, giving rise to various opinions from the public regarding the Mixue outlet which is growing so rapidly. So from that, sentiment analysis will be carried out by classifying using the implementation of the Support Vector Machine (SVM) algorithm. From the results of research that has been done, the SMOTE-based Support Vector Machine (SVM) algorithm results in an increase in the accuracy value to 73.67% and precision to 75.40%, and for the results of Support Vector Machine (SVM) without using SMOTE, the accuracy value is 69.40% and the precision value is 68.12%. But on the contrary there was a decrease in the recall value to 70.83% and the F1-Score value to 72.79%. So from the evaluation results it can be concluded that SMOTE has an effect on increasing the accuracy and precision values, but there is a decrease in the recall value and F1-Score.
ADOPSI DEVSECOPS UNTUK MENDUKUNG METODE AGILE MENGGUNAKAN TRIVY SEBAGAI SECURITY SCANNER DOCKER IMAGE DAN DOCKERFILE Perkasa, Panca Rizki; Mailoa, Evangs
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 3 (2023): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i3.291

Abstract

Compliance with personal data protection laws requires electronic system operators to pay more attention to security in applications. Security testing which is usually done at the end of the SDLC makes Agile principles incompatible with advantages that prioritize acceleration, adaptability and responsiveness to change. DevSecOps implementation using Trivy will insert a security scanner process for applications that are deployed in containerized form. The continuous process of security scanning integrated in CI/CD will increase the awareness of developers in terms of application security, so that developers will more quickly fix these problems and avoid security problems at the end of the SDLC.
Pengembangan Sistem Oracle Forms untuk Optimisasi Proses Penjualan di PT.XYZ G.A, Ambrosius Ludang; Mailoa, Evangs
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 2 (2024): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i2.711

Abstract

This research focuses on the development of an efficient sales system for PT. XYZ a retail company in Indonesia. The company faces challenges in its sales processes due to the lack of efficiency in the manual system, which is susceptible to errors. The research aims to optimize the sales process by utilizing Oracle Forms 10g and implementing the Rapid Application Development (RAD) methodology to ensure timely solutions. The developed system demonstrates a significant improvement in efficiency, accuracy, and speed. The use of Oracle Forms and SQL Developer simplifies data entry and management processes, while the implementation of List of Values (LOV) reduces errors and enhances data consistency. Furthermore, the system provides real-time insights into sales performance, enabling better decision-making and strategic planning for the company.
Pembuatan Website REST API Ensiklopedia Tumbuhan dengan Kombinasi Framework Spring Boot dan MyBatis Generator Hartono, Michael Antonius; Mailoa, Evangs
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 2 (2024): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i2.712

Abstract

This research focuses on the development of the Plant Encyclopedia REST API, a website application designed using Java, HTML, Spring Boot, MyBatis Generator, and Swagger technologies, with PostgreSQL as the database used to store plant data from the API. This study highlights the declining abilities of the younger generation, especially in mathematics, literacy, and science. Statistical data from PISA test scores from 2003 to 2022 on 15-year-olds worldwide show a peak decline in 2022. The applied research methods include needs analysis, problem identification, system design, application design, implementation, and system testing. This research aims to overcome the lack of knowledge about plants that can have a negative impact on the ecosystem and provide various other benefits. The result of this research is a plant encyclopedia website application that can be accessed by various users to find information related to plants and a swagger website for administrators to manage plant data.
Pembuatan REST API Manajemen Data Karyawan Berbasis Website Menggunakan Spring Boot Adji, Leonard Surya Adji; Mailoa, Evangs
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 2 (2024): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i2.713

Abstract

This study explores the implementation of REST API with Spring Boot in managing employee data in an E-commerce company. With a focus on efficiency, accuracy, and data security, the application utilizes Java, Spring Boot, and MyBatis as the main foundations. System testing demonstrates success in searching, creating, updating, and deleting employee data. Through the Model View Control (MVC) structure, the application ensures clear task separation. This success affirms the application as an effective solution for employee data management, particularly in the dynamic E-commerce industry.
Pendekatan Data Science terhadap Pemilu 2024: Memahami Persepsi Publik dan Tren Opini Politik Madika, Fiwi Fishinsky; Mailoa, Evangs
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 3 (2024): Edisi Juli
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i3.437

Abstract

In this study, Twitter is used as a data source to analyze sentiment and public opinion related to the 2024 General Election. The sentiment analysis method is employed to understand how public views are reflected in tweets containing relevant hashtags. The research aims to identify trends in political opinion and public perception that emerge on Twitter during a specific period before, during, and after the election. By analyzing relevant tweet data, this study will provide in-depth understanding of how public opinion evolves and changes over time, as well as identifying the most influential and popular accounts in political discussions on Twitter. The results show that the majority of public opinion about the 2024 General Election on Twitter is positive, with dominant support and sympathy for presidential candidates. Social network analysis reveals a well-structured network with @eternaciumentaa being the most influential and @geloraco being the most popular
Implementasi Time Series Dalam Prediksi Jumlah Keberangkatan Penumpang di Pelabuhan Tanjung Perak Fernando, Zuriel; Mailoa, Evangs
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 13, No 2: Agustus 2024
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v13i2.1998

Abstract

Tanjung Perak Port has an important position in the transportation network, this causes Tanjung Perak Port to have busy activities and operations. The need for the number of passenger seats is very important to support passenger comfort. To overcome this problem, the port of Tanjung Perak requires predictions of the number of passengers who will depart. This research aims to predict the number of passenger departures from Tanjung Perak Port for a period from January 2024 to December 2024 by analyzing previous data from January 2006 to December 2023. This research applies the Time Series Triple Exponential Smoothing (Holt-Winters) method. Multiplicative model in predicting the number of passengers departing on domestic cruises at Tanjung Perak Port, Surabaya. The results of this research predict that there will be an increase in the number of passengers departing from Tanjung Perak Port in July 2024 by 60,042 people and in August 2024 by 68,210 people, and a decrease in the number of passengers in April 2024 by 33,921 people.Keyword: Prediction; Time Series; Marine Transportation AbstrakPelabuhan Tanjung Perak memiliki posisi penting dalam jaringan transportasi, hal ini menyebabkan Pelabuhan Tanjung Perak memiliki aktivitas dan operasional yang padat. Kebutuhan jumlah kursi penumpang sangat penting untuk menunjang kenyamanan para penumpang. Untuk mengatasi permasalahan tersebut, pelabuhan Tanjung Perak membutuhkan prediksi jumlah penumpang yang akan melakukan keberangkatan. Penelitian ini bertujuan untuk melakukan prediksi jumlah keberangkatan penumpang dari Pelabuhan Tanjung Perak dengan jangka waktu dari bulan Januari 2024 hingga bulan Desember 2024 dengan melakukan analisis data sebelumnya pada Januari 2006 sampai Desember 2023. Penelitian ini menerapkan metode Time Series Triple Exponential Smoothing (Holt-Winters) model Multiplikatif dalam memprediksi banyak penumpang yang berangkat pada pelayaran domestik di Pelabuhan Tanjung Perak, Surabaya. Hasil penelitian ini memperoleh prediksi mengalami kenaikan jumlah penumpang yang berangkat dari Pelabuhan Tanjung Perak di bulan Juli 2024 sebanyak 60.042 orang dan bulan Agustus 2024 sebanyak 68.210 orang, dan mengalami penurunan jumlah penumpang pada bulan April 2024 sebanyak 33.921 orang. 
Peramalan Jumlah Kedatangan Wisatawan Mancanegara dengan Menggunakan Model ARIMA Cristanto, Mordekhai; Mailoa, Evangs
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 13, No 2: Agustus 2024
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v13i2.1979

Abstract

Tourism is the main source of economic development in Bali Province. The Ministry of Tourism and Creative Economy targets 7 million tourists in 2024. The inaccuracy of tourist arrival forecasting processes has negatively impacted business growth and tourism revenue. Therefore, it is necessary to identify factors related to the arrival of foreign tourists to Bali to improve forecasting accuracy and stimulate economic growth. This research is conducted to forecast the number of foreign tourists arriving in 2024, aiming to assist in meeting the established targets. The forecast is performed using the Time Series ARIMA model with variables processed from historical data from 2009 to 2023. Through the ARIMA model, the forecasted number of foreign tourists arriving at Ngurah Rai Airport is 6,243,210, with the smallest Mean Square Error (MSE) being 1899.60. This underscores the need for innovation in tourism promotion and development of tourist destinations in Bali.Keywords: Prediction; Time Series; Bali Tourism; ARIMA. AbstrakPariwisata merupakan sumber utama pembangunan perekonomian di Provinsi Bali. Kementerian Pariwisata dan Ekonomi Kreatif menargetkan 7 juta wisatawan pada tahun 2024. Ketidaktepatan proses peramalan kedatangan wisatawan selama ini telah berdampak negatif terhadap pertumbuhan bisnis dan pendapatan dari pariwisata. Oleh karena itu, perlu dilakukan identifikasi faktor-faktor yang berhubungan dengan kedatangan wisatawan asing ke Bali untuk meningkatkan ketepatan peramalan dan mendorong pertumbuhan ekonomi. Penelitian ini dilakukan untuk meramalkan jumlah wisatawan asing yang datang pada tahun 2024, guna membantu dalam memenuhi target yang telah ditetapkan. Peramalan dilakukan dengan menggunakan metode Time Series model ARIMA dengan variabel yang diproses meliputi data historis tahun 2009 sampai dengan tahun 2023. Melalui model ARIMA hasil peramalan kedatangan wisatawan asing khususnya di Bandara Ngurah Rai didapatkan jumlah 6.243.210 wisatawan dengan nilai Mean Square Error (MSE) terkecil adalah 1899,60, maka diperlukannya inovasi pada bidang promosi pariwisata dan diperlukan pengembangan destinasi wisata di Bali. 
Perbandingan Beberapa Algoritma Machine Learning Dalam Analisis Sentimen Terkait Pemilihan Presiden RI 2024 Nopan, Nopan; Mailoa, Evangs
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 13, No 2: Agustus 2024
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v13i2.1980

Abstract

The 2024 Presidential Election has become a hot topic among the Indonesian community. People often share their opinions and criticisms openly on social media, such as Twitter or X, leading to debates among the public in supporting a presidential candidate. These debates often result in various public comments that are positive, neutral, or negative. Sentiment analysis is used as a method to analyze public opinions and comments using machine learning algorithms. The purpose of this research is to compare the accuracy levels of the Support Vector Machine, Naive Bayes, and Decision Tree algorithms. This research uses a dataset from Twitter, which will go through stages of data merging, text preprocessing, translation, labeling, and algorithm classification. The results of this research show that the Support Vector Machine algorithm has a higher accuracy rate than the other two algorithms, with an accuracy rate of 81.49%.Keywords: Election 2024; Sentiment Analysis; Support Vector Machine; Naive Bayes; Decision Tree.AbstrakPemilihan Presiden 2024 menjadi topik yang hangat dikalangan masyarakat indonesia, masyarakat sering membagikan pendapat dan kritik secara terbuka dimedia sosial twitter atau X, yang menyebabkan subjek perdebatan bagi masyarakat dalam mendukung salah satu kandidat presiden, dampak dari perdebatan ini sering memunculkan berbagai komentar masyarakat yang bersifat positif, netral maupun negatif. Analisis sentimen digunakan sebagai metode untuk menganalisis tentang pendapat dan komentar masyarakat dengan penggunaan algoritma machine learning. Tujuan dari penelitian ini dilakukan untuk membandingkan tingkat akurasi dari algoritma Support Vector Machine, Naive Bayes dan Decision Tree, penelitian ini menggunakan dataset dari twitter yang akan diproses melalui tahapan penggabungan data, preprocessing text, translate, pelabelan dan klasifikasi algoritma. Hasil dari penelitian ini menunjukan bahwa algoritma Support Vector Machine memiliki tingkat akurasi yang lebih tinggi dari kedua algoritma lainnya dengan nilai akurasi 81.49%. 
Analisis Sentimen terhadap RSUD Salatiga Menggunakan SVM dan TF-IDF Azzahra, Windy Livia; Mailoa, Evangs
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 1 (2025): Januari
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v6i1.1208

Abstract

The Salatiga Regional General Hospital (RSUD) plays an important role in providing healthcare services. This research analyzes public sentiment towards RSUD Salatiga using the SVM method with a linear kernel for sentiment analysis and TF-IDF for feature extraction. The dataset consists of 414 processed reviews, including case folding, data cleaning, tokenization, normalization, stopword removal, and stemming. Evaluation shows that the model achieved an accuracy of 84.00%; precision of 84.00%; recall of 83.25%; and an F1-score of 83.53%. A total of 55.8% of reviews indicated positive sentiment and 44.2% negative sentiment, highlighting the need for improvements in the queue system, waiting times, and parking facilities. The SVM and TF-IDF methods were chosen for their ability to handle large text data with high accuracy. This research provides practical contributions in the form of recommendations such as the implementation of a technology-based queue system. Limitations include the limited amount of data and platform bias, so exploring other algorithms, such as Naive Bayes and Random Forest, is recommended.
Co-Authors Ade Iriani Ade Kresna Dewantara Adeline Debby Christiana Adi Nugroho Adilla, Denise Sheryl Adinda Setya Oktami Adji, Leonard Surya Adji Andreas A. Sukmana Anugramidah Limbong Madika Aprillia Stefvani Tetikay Arbitra Satria Perdana Arief Wijanarko Arif Hasan Nawawi Azzahra, Windy Livia Bernardus Redika Westama Putra Betty E. Kristiani Bintang Samasto, Revo Budhi Kristianto Candrawira Wicaksana, Gede Abdi Christofael Natalio Dondan Cristanto, Mordekhai Danny Manongga Deasy Carolina Dewi, Teresia Ardika Dhinora, Monica Yoshe Dwi Hosanna Bangkalang Epliani Limbong Rara Erwien Christianto Fairiani, Ayuquinn Astuticein Faqih, Muhammad Faiq Adhitya Fernando, Zuriel Fredrik Landjamara Ndjurumana G.A, Ambrosius Ludang Gabriel Mika Angelo Gede Abdi Candrawira Wicaksana Gilang Agung Saputra Hanita Yulia Harnanda, Elifas Gavra Hartono, Michael Antonius Harun B S O. Mosioi Hendry, - Hindriyanto Dwi Purnomo Hutami Jane Antonia .M Irwan Sembiring Jacob Daan Engel Joseph Triwin Subarjo Juan Rondor Jonathan Sumakul Krisetianto, Wijaya Yoga Krismiyati Krisna Adi Saputra Krisna Setiawan Luhukay, Priscila Karen Madika, Fiwi Fishinsky Magdalena Ariance Ineke Pakereng Muhammad Arifin Muhammad Rizky Pribadi Natacia, Fanny Nina Setiyawati Nopan, Nopan Pakan, Raynaldo Panca Rizki Perkasa Perkasa, Panca Rizki Pramastya, Pragnanta Yopie Putri, Tiara Syah Indra Radius Tanone Santoso, Fian Julio Sri Kasmiyati Sugeng Ariyadi, Faisal Nuryawan Susanti, Era Syahreza Triadhana Marsudi Tad Gonsalves Teguh I. Bayu Teresia Ardika Dewi Tesalonika, Ribka Ventje Jeremias Lewi Engel Widyasari, Elvira Resti Widyastuti, Fransisca Sonia WIJANARKO, ARIEF Willson Mangoki