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All Journal Seminar Nasional Informatika (SEMNASIF) JIKO (Jurnal Informatika dan Komputer) Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) IKRA-ITH Informatika : Jurnal Komputer dan Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) IKRA-ITH ABDIMAS Syntax Idea International Journal of Informatics and Computation JISA (Jurnal Informatika dan Sains) JIKA (Jurnal Informatika) International Journal of Research in Community Services Jurnal Informatika Terpadu Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Formosa Journal of Science and Technology (FJST) Jurnal Pengabdian Masyarakat Bestari (JPMB) Formosa Journal of Applied Sciences (FJAS) Jurnal Keguruan dan Ilmu Pendidikan Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Universal Raharja Community (URNITY Journal) Jikom: Jurnal Informatika dan Komputer Jurnal Elektronika dan Teknik Informatika Terapan JI-Tech Jurnal PETISI (Pendidikan Teknologi Informasi)
Barus, Simon Prananta
Matana University
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Implementation of The Patas Model in The Development of The Matana University Graduation Information System Simon Pranata Barus
JISA(Jurnal Informatika dan Sains) Vol 5, No 2 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i2.1327

Abstract

There is an urgent need to make a graduation information system at Matana university. This system is needed, especially for students, secretariat, and finance. Several other things to consider, namely the number of participants and the graduation committee are more than 300 people, delegation of system development to one person, many users are still unfamiliar with integrated information systems based on information technology (IS/IT), the Corona pandemic, and can anticipate sudden changes. Therefore, we need a software development model that fits these conditions. The research methods for making this model, such as literature study, interviews, observation, model making and model testing (model application). This software development model is called Patas, it is fast and limited. This model has six stages, i.e., user needs, tools selection, modification, evaluation, implementation, and maintenance. This model is then applied to the development of a graduation information system at Matana University. The Matana University graduation information system has been successfully built using the Patas model. This system features prospective graduate registration, payment, sending information to participants (personal or mass) by email, dashboard, privilages user, attendance for graduates by barcode, change profile and content management. This graduation information system is used from graduation preparation to graduation day. Thus, the Patas model can be an alternative model in software development. In the future, the Patas model needs to be tested for the development of a different and larger system. 
Comparison of Prediction of the Number of People Exposed to Covid 19 Using the Lagrange Interpolation Method with the Newton Gregory Maju Polynomial Interpolation Method F. Anthon Pangruruk; Simon P. Barus
Formosa Journal of Applied Sciences Vol. 2 No. 6 (2023): June 2023
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjas.v2i6.4853

Abstract

In March 2020 the World Health Organization stated that the Corona Virus pandemic (Covid-19) was due to its massive spread and hit all countries in the world. Academics and practitioners are called upon to carry out research activities in order to obtain a mathematical model that can be used to predict the number of people exposed to Covid-19 or other diseases. The researchers previously tried research to predict the number of people exposed to Covid-19 from early 2021 using the Monte Karlo method, the Hybrid Nonlinear Regression Logistic– Double Exponential Smoothing method, the Arima method, the BackPropagation and Fuzzy Tsukamoto methods, the K-Nearest method. Neighbors, Time Series Analysis method, Winter Method and Long Short Time Memory (LSTM) Artificial Neural Network method.
Web Training by Using HTML and CSS to Attract Interest in Learning Programming for High School Students Prya Artha Widjaja; Simon Prananta Barus; Ary Budi Warsito; Jose Ryu Leonesta; Sabrina Yose Amalia; Veronica Yose Ardilla; Nico Abel Laia
Jurnal Pengabdian Masyarakat Bestari Vol. 2 No. 6 (2023): June 2023
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/jpmb.v2i6.4476

Abstract

In digital field, the need for programmers is increasing, but quality resources in the field of programming are still lacking. Many schools, in this case, high schools, have not taught programming in the school curriculum. This training is given to attract students' interest in learning programming. The chosen method is to teach how to make front-end views of web pages. This method was chosen because web programming is easier to learn and participants can see the results displayed live. From the activities that have been carried out, the participants were very enthusiastic about participating and asked for further training. It can be concluded that this training succeeded in attracting the interest of participants, namely high school students, to learn programming, specifically web programming.
Chatbot Pemasaran Untuk Merespon Pertanyaan Calon Mahasiswa Baru Simon Prananta Barus; Ary Budi Warsito
Jurnal JI-Tech Vol 18 No 1 (2022): Jurnal JI-Tech
Publisher : Sekolah Tinggi Teknologi Informasi NIIT

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

Abstract

Era revolusi industri 4.0 memicu lahirnya berbagai macam inovasi. Inovasi ini didukung oleh kehadiran teknologi, seperti kecerdasan buatan (artificial intelligence), IoT, maha data (big data), komputasi awan (cloud computing), pencetakan 3D (3D printing). Saat pandemi Corona, pengguna Internet melonjak drastis. Lonjakan ini disebabkan banyak aktivitas yang dahulu dilakukan luring berpindah ke daring, seperti belajar, bekerja, belanja, bertemu dan beribadah. Ini berakibat ruang gerak marketing menjadi terbatas, khususnya untuk berinteraksi dengan calon mahasiswa baru. Untuk dapat mengatasi keterbatasan ini dibutuhkan chatbot sebagai asisten marketing, khususnya untuk memberikan respon pada setiap pertanyaan yang sering ditanyakan (Frequently Asked Questions (FAQ)). Chatbot (CSbot) ini tidak hanya dapat diakses tiap saat, tapi juga dimana saja selama terkoneksi ke Internet. Google menyediakan platform natural language understanding (NLU), Dialogflow, melalui komputasi awan yang dapat membantu pengembangan chatbot agar lebih mudah dan cepat. Metode penelitian yang dilakukan, yaitu studi literatur, wawancara, observasi, pengembangan aplikasi chatbot dengan model prototyping (berisikan tahapan kebutuhan pengguna, prototipe sistem / sub sistem, evaluasi prototipe, penyempurnaan prototipe, pengujian sistem, implementasi sistem, dan perawatan sistem). Aplikasi CSbot telah selesai dibuat dengan menggunakan Dialogflow. Selanjutnya, CSbot ini dapat dikembangkan dengan penambahan dialog, antisipasi penggunaan bahasa asing, bahasa daerah atau singkatan – singkatan yang umum, fasilitas untuk mengisi form pendaftaran, dan penyempurnaan dialog berbasis suara
Klasterisasi Hewan berdasarkan Morfologi dengan K-Means Klastering untuk Memudahkan Pemahaman Taksonomi Hewan Pangestu, Timothy Daniel Pangestu; Ardila, Veronica Yose Ardila; Marco Suteja; Simon Prananta Barus
Jurnal Informatika dan Komputer Vol 14 No 2 (2024): Oktober
Publisher : Sekolah Tinggi Ilmu Komputer PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55794/jikom.v14i2.145

Abstract

Grouping animals based on their morphological characteristics is crucial for understanding biodiversity and evolutionary relationships among species. This study aims to apply the K-Means Clustering algorithm to group animals based on their morphological characteristics and evaluate its performance. A secondary dataset from Kaggle containing 101 animals with 18 morphological attributes was used. Data preprocessing techniques such as handling missing data, removing irrelevant columns, and data normalization were performed. The optimal number of clusters was determined using the Davies-Bouldin Index and Silhouette Score, which resulted in 5 clusters as the best number. The K-Means algorithm successfully grouped the data into 5 clusters: flying animals with feathers, aquatic animals with fins, terrestrial mammals with hair and milk production, animals with many legs such as reptiles and insects, and terrestrial predators. Visualization of the clustering results using 3D scatter plots provided a clear visual representation. Interpretation of the results revealed patterns and evolutionary relationships between different groups of animals based on their morphological characteristics. This research contributes to the understanding of biodiversity, ecology, evolution, and conservation through morphology-based clustering, and demonstrates the effectiveness of the K-Means algorithm in this task.
Rancang Bangun Aplikasi Pengelola Smart Engine Untuk Deteksi Jenis Biji Kopi Dengan Menerapkan Web Service Kenneth Liem Hardadi; Simon Prananta Barus
Jurnal Elektronika dan Teknik Informatika Terapan ( JENTIK ) Vol. 1 No. 2 (2023): Juni: Jurnal Elektronika dan Teknik Informatika Terapan (JENTIK)
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/jentik.v1i2.297

Abstract

Coffee is one of the agricultural commodities that plays a vital role as a refreshing beverage and is categorized as a perennial crop. Currently, coffee is becoming a trend and an integral part of people's lifestyles. Coffee is divided into two main varieties: robusta and arabica, each with its distinct characteristics. Differentiating between robusta and arabica coffee beans can be challenging due to their similar physical appearance. Deep learning-based smart engines can offer a solution to this problem, although their complex user interfaces often hinder accessibility. To address this issue, a Spiral development method was employed to build an application using PHP programming language and Codeigniter 4 framework. This application facilitates easy detection of coffee bean types through both a website and a RESTful API, allowing users to access it online from any device. The application underwent comprehensive black box testing, demonstrating successful functionality aligned with the initial design objectives. It is expected that this application will solve the identified problem and provide significant assistance to coffee enthusiasts and the general public in easily distinguishing between various coffee bean types. Users can effortlessly recognize and differentiate between different coffee beans while obtaining useful information about their preferred coffee beans.
Prediksi Harga Saham dengan Interpolasi Polinom Newton Gregory Maju Pangruruk, F. Anthon; Barus, Simon Prananta
Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya 2018: Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1513.829 KB)

Abstract

Harga saham bersifat fluktuatif sehingga dibutuhkan suatu model untuk memprediksi harga saham. Interpolasi polinom Newton Gregory Maju berderajat 3 merupakan salah satu model dalam metode numerik yang dapat digunakan untuk memprediksi harga saham sehingga para pemain saham dapat mengambil keputusan yang tepat. Data masukan berupa harga saham pembukaan (open) dan luarannya berupa harga saham penutupan (close) sebagai hasil prediksi dari Perusahaan Astra Agro Lestari Tbk (AALI) di Bursa Efek Indonesia pada bulan Oktober hingga Desember 2017. Data ini terlebih dahulu dilakukan penghalusan (smoothing) dengan menggunakan metode moving average (MA). Selanjutnya dilakukan regresi linier untuk memperoleh persamaan regresinya. Tahap berikutnya dibuat barisan harga saham open dengan beda yang sama dalam interval tertentu sebagai variabel independen, sehingga diperoleh hasil harga saham close regresi. Dari proses tersebut diperoleh tabel selisih maju (forward difference) yang dapat dipakai berulangulang untuk memprediksi harga saham close yang berjalan dengan program komputasi berbasis java. Untuk mendapatkan hasil saham close prediksi dibutuhkan harga saham open yang berjalan. Hasil harga prediksi saham close dengan harga saham close riilnya dibandingkan dan diperoleh persentage error sangat kecil, yang berarti harga saham close prediksinya cukup mendekati harga saham close yang sesungguhnya.
Rancang Bangun Aplikasi Mobile Foodaround dengan Augmented Reality untuk Memperkenalkan Makanan Tradisional Betawi Pangestu, Timothy Daniel; Barus, Simon Prananta
Jurnal Informatika Terpadu Vol 10 No 2 (2024): September, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v10i2.1293

Abstract

Indonesia has a rich culinary cultural heritage, including traditional Betawi cuisine. However, this culinary heritage is increasingly being eroded among the younger generation. This research aims to develop a mobile application named FoodARound that uses markerless Augmented Reality (AR) technology to introduce traditional Betawi food to a broader audience. The application visualizes 3D objects of foods such as kerak telor, kue cucur, and selendang mayang, and provides information on the ingredients and how to make them. A prototyping method was used in the development of the application, including needs analysis, prototype creation, prototype evaluation, and implementation. Unity and Vuforia were used as the AR application development platforms, while Firebase Realtime Database was integrated to store food data. Black box testing was conducted to validate the functionality of the application. The research results show that the FoodARound application was successfully developed and can be used as an interactive medium to introduce traditional Betawi food to the public, especially the younger generation. AR technology provides an engaging experience for learning about local culinary heritage. This research highlights the importance of technological innovation in preserving culture and traditional culinary education.
Sentiment Analysis on Google Reviews Using Naïve Bayes, K-Nearest Neighbors, and Logistic Regression to Improve Novotel Services Dhamma, Yonathan Arya; Barus, Simon Prananta
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8923

Abstract

The application of artificial intelligence (AI) has been widely used in various industrial sectors, including the hospitality industry. One of the applications that is widely used in the hospitality industry is sentiment analysis. Sentiment analysis is carried out by analyzing feedback data from hotel guests or customers. The results of this sentiment analysis are important for decision makers to improve and improve their services. This study aims to obtain sentiment analysis results from Novotel hotel Google reviews based on machine learning by comparing three algorithms, namely Naïve Bayes, K-Nearest Neighbors (KNN), and Logistic Regression. The stages carried out in this study are data collection, data labeling, exploratory data analysis (EDA), data preprocessing, text representation, data sharing, modeling, model training, model evaluation, selection of the most accurate model, visualization of the most accurate model, interpretation of results and writing research reports. The dataset used was 1200 reviews, only 1190 reviews were used in the analysis. From the training results, the model produced by the Logistic Regression algorithm was the most accurate, namely 94.54% with unigrams (n = 1). Here are the results of each category, positive as many as 723 reviews (60.76%), negative as many as 218 reviews (18.32%), and neutral as many as 249 reviews (20.92%). Thus, most of the sentiment towards the service is positive, but some services need to be fixed and improved for customer satisfaction. The next research, the research area is expanded and the use of Deep Learning.
Analisis Sentimen YouTube: "Di Balik Ambisi Jokowi dalam IKN" Abel Laia, Nico; Barus, Simon Prananta
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 5 No 1 (2025): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v5i1.891

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen penonton terhadap video YouTube "Di Balik Ambisi Jokowi dalam IKN" yang diunggah oleh kanal Narasi Newsroom. Analisis dilakukan dengan mengumpulkan dan mengolah 3.000 komentar dari video tersebut menggunakan teknik Natural Language Processing (NLP). Metode yang digunakan mencakup pengumpulan data, pembersihan data, ekstraksi fitur, serta klasifikasi sentimen ke dalam kategori positif, negatif, dan netral. Model pembelajaran mesin yang diterapkan dalam penelitian ini meliputi Decision Tree, Naïve Bayes, dan Support Vector Machine (SVM). Hasil penelitian menunjukkan bahwa model Decision Tree memberikan akurasi terbaik dibandingkan model lainnya dalam mengklasifikasikan sentimen komentar. Analisis lebih lanjut mengungkapkan bahwa opini publik terhadap ambisi Jokowi dalam proyek IKN beragam, dengan sentimen yang berkisar dari netral hingga sangat tidak mendukung. Temuan ini memberikan wawasan mengenai persepsi masyarakat terhadap kebijakan pemindahan ibu kota.
Co-Authors Abel Laia, Nico Anthony, Eveline Valencia Anton F. Pangruruk Ardila, Veronica Yose Ardila Ary Budi Warsito Ary Budi Warsito Az-zahra, Adinda Safitri Bakti Siregar Celvin, Jullius Dhamma, Yonathan Arya F. Anthon Pangruruk F. Anthon Pangruruk Geraldo Julius Halim Gulo, Imanuel Andreas Jong, Novesa Erick Jose Ryu Leonesta Kenneth Liem Hardadi Kenneth Liem Hardadi Laia, Nico Abel Leonesta, Jose Ryu Marco Suteja Nico Abel Laia Pangestu, Timothy Daniel Pangestu, Timothy Daniel Pangestu Pangruruk, F. Anthon Paramitha Gunawan Prya Artha Widjaja Sabrina Yose Amalia Sabrina Yose Amelia Saranani, Fiola Arta Glorya Seo, Jeriko Ichtus Stanley Tejadinata Sujanto, Sujanto Tangdililing, Matius Monto Lungan Veronica Yose Ardilla Widjaya, Prya Artha Artha