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Analisis Sentimen Terhadap Aplikasi Satu Sehat Pada Twitter Menggunakan Algoritma Naive Bayes Dan Support Vector Machine Rasiban, Rasiban; Riyadi, Sugeng
Jurnal Sains dan Teknologi Vol. 5 No. 3 (2024): Jurnal Sains Dan Teknologi
Publisher : CV. Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/saintek.v5i3.2790

Abstract

Berdasarkan pencarian di media sosial Twitter, Peneliti mengidentifikasi beberapa masalah yang sering disorot oleh masyarakat terkait Isu Stadion Jakarta Internasional Stadium (JIS) Belum berstandar FIFA. Isu ini dilontarkan pertama kali oleh Hunter Jagal, Postingan akun media sosial twitter @hunterjagar3 dengan Konten Tweet “Kaesang pengarep berkomentar terkait pssi yang menyebut Jakarta Internasional Stadium belum berstandar FIFA. Maka Peneliti melakukan Analisis Sentimen Opini Masyarakat Terhadap Stadion Jakarta Internasional Stadium (Jis) Pada Twitter Dengan Perbandingan Metode Naive Bayes Dan Support Vector Machine.Hasil akhir dari Perbandingan dengan dua metode pengujian ini, yaitu hasil prediksi Sentimen Masyakarat Terhadap Isu Stadion Jakarta Internasional stadium belum berstandar FIFA berdasarkan data yang didapat dari Twitter dan diimplementasikan dengan metode Naive Bayes menunjukkan nilai akurasi sebesari 99.57%. Dari 940 data uji, terprediksi sebesar 892 data sebagai Prediksi Sentimen Negatif dan 48 data sebagai Sentimen Positif dan Metode Support Vector Machine menunjukkan nilai akurasi sebesari 99.68%. Dari 940 data uji, terprediksi sebesar 894 data sebagai Sentimen Negatif dan 46 data sebagai Sentimen Positif.
Analisis Sentimen Opini Masyarakat Terhadap Stadion Jakarta Internasional Stadium (Jis) Pada Twitter Dengan Perbandingan Metode Naive Bayes Dan Support Vector Machine Rasiban, Rasiban; Riyadi, Sugeng
Jurnal Sains dan Teknologi Vol. 5 No. 3 (2024): Jurnal Sains Dan Teknologi
Publisher : CV. Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/saintek.v5i3.2962

Abstract

Berdasarkan pencarian di media sosial Twitter, Peneliti mengidentifikasi beberapa masalah yang sering disorot oleh masyarakat terkait Isu Stadion Jakarta Internasional Stadium (JIS) Belum berstandar FIFA. Isu ini dilontarkan pertama kali oleh Hunter Jagal, Postingan akun media sosial twitter @hunterjagar3 dengan Konten Tweet “Kaesang pengarep berkomentar terkait pssi yang menyebut Jakarta Internasional Stadium belum berstandar FIFA. Maka Peneliti melakukan Analisis Sentimen Opini Masyarakat Terhadap Stadion Jakarta Internasional Stadium (Jis) Pada Twitter Dengan Perbandingan Metode Naive Bayes Dan Support Vector Machine.Hasil akhir dari Perbandingan dengan dua metode pengujian ini, yaitu hasil prediksi Sentimen Masyakarat Terhadap Isu Stadion Jakarta Internasional stadium belum berstandar FIFA berdasarkan data yang didapat dari Twitter dan diimplementasikan dengan metode Naive Bayes menunjukkan nilai akurasi sebesari 99.57%. Dari 940 data uji, terprediksi sebesar 892 data sebagai Prediksi Sentimen Negatif dan 48 data sebagai Sentimen Positif dan Metode Support Vector Machine menunjukkan nilai akurasi sebesari 99.68%. Dari 940 data uji, terprediksi sebesar 894 data sebagai Sentimen Negatif dan 46 data sebagai Sentimen Positif.
PENERAPAN ALGORITMA K-MEANS UNTUK ANALISIS NILAI TAHFIDZ SANTRI BERDASARKAN NILAI FASHOHAH DAN TAJWID (STUDI KASUS : PESANTREN MODERN AT-TAQWA BOGOR) Najib, Muhammad Alfin; Rasiban
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.v4i3.392

Abstract

Modern Islamic boarding schools, like other Islamic boarding schools, are part of Islamic education in Indonesia. The purpose of this research is to develop a system that uses the K-Means Clustering method to analyze the tahfidz values ​​of students based on fashohah and tajwid values. This system utilizes the RapidMiner Application as a testing tool. This research has several important contributions. First, by applying the K-Means Clustering method, the system can group students based on similar tahfidz abilities. This can help educators (Tendik) Modern Islamic Boarding School At-Taqwa Gunungputri Bogor in monitoring and evaluating the progress of students in memorizing the Qur'an. With this research, the Leaders of the At-Taqwa Modern Islamic Boarding School hope that the New Santri Acceptance Committee (PSB) and especially the Educators of the At-Taqwa Modern Islamic Boarding School can be useful and assist them in determining the value of Tahfidz and can improve the quality of the Tahfidz Program at this Islamic Boarding School
ANALISIS SENTIMEN KEUANGAN (DATA FIQA AND FINANCIAL PHRASEBANK) MENGGUNAKAN ALGORITMA LOGISTIC REGRESSION DAN SUPPORT VECTOR MACHINE Hutagalung, Julinar Sari; Rasiban
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.v4i3.404

Abstract

Finance is a very vital sector in a Company and institution because it has a very important strategic role in creating a conducive environment, especially for the improvement of the national economy. Through a combination of FiQA and Financial PhareBank text datasets, an analysis of positive, negative and neutral sentiments related to finance is carried out that can be taken into consideration to make a policy in the financial sector or context in achieving this strategic role. Application of sentiment analysis using hyperparameter tuning in Logistic Regression and Support Vector Machines algorithms, with TF-IDF and Smote weighting on training data. The best model results of 70.70% accuracy on the Support Vector Machine algorithm during model training using training data that is not done Smote class imbalance.
Implementasi Data Mining dengan Metode Regresi Linear untuk Prediksi Hasil Penjualan di PT Awitama Cyndo Wahana Pradita, Anggi; Rasiban
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 3 (2024): 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.v5i3.957

Abstract

The increase in the number of new entrepreneurs in Indonesia is influenced by technological developments, easier access to information, and government support. This increases the need for legal services such as making business permits and business agreements. Lack of understanding of the legal aspects of business can hinder business growth. PT Awitama Cyndo Wahana, a legality management service provider, has great potential to support new entrepreneurs. However, this company has not utilized sales data to predict future sales. This research uses sales data from PT Awitama Cyndo Wahana to predict sales using the linear regression method. The test results show that the Root Mean Squared Error (RMSE) is 458618.289, the Absolute Error is 183084.456, and the Relative Error is 0.98%. These results indicate that the prediction model has an adequate level of accuracy, although there are some prediction errors. The practical implication of this research is that PT Awitama Cyndo Wahana can use these sales predictions to plan more effective marketing strategies and make data-based business decisions, so that they can better support the growth of new entrepreneurs.
Pengembangan Media Pembelajaran Berbasis Teknologi Augmented Reality (AR) dengan Algoritma Vuforia SDK pada Mata Pelajaran IPA Kelas VIII di Madrasah Al-Aqsha (MTS) Aryanti, Putri Gea; Rasiban; Sarimole, Frencis Matheos; Tundo
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 3 (2024): 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.v5i3.998

Abstract

This research aims to develop learning media in the form of an Android-based Augmented Reality application on human respiratory system material which can provide information about the introduction of respiratory system organs and their processes, and markerless images of the shapes of the organs which have been input into the Vuforia SDK library. This research develops an Augmented Reality application as an introduction to the organs of the respiratory system using several tools such as: MDLC, Unity, ARToolkit to better understand students about the respiratory system. The application of Augmented Reality in this research uses the Marker Based methods. The Augmented Reality application for recognizing the respiratory system in humans was tested using BlackBox with results of passing the system functional test 100% and usability testing results using a questionnaire. The learnability aspect was 4.47, efficiency 4.43, memorability 4.2, errors 4.5, and satisfaction 4.52, this application was tested in the "Good" category. Although there are problems with the quality of the camera and lighting, the test results show that this application is interesting and worthy of being developed again, so the implementation of Augmented Reality on MTS Al-Aqsa is the right step to support the method concept learning.
Sistem Informasi Pembelajaran Taman Pendidikan Al-Qur’an di Yayasan Al-Muttaqien Jadid (TPQ) Rasiban; Hanif, Zuhdi; Sumabrata, Raden Muhammad Jachfitrah Ardhi; Yuliansyah, Ahmad Fauzan
Jurnal Pengabdian Nasional (JPN) Indonesia 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/jpni.v5i2.679

Abstract

This research aims to determine the TPA academic system currently running at Al-Muttaqien Jadid. This research is descriptive research using qualitative methods. Data collection was carried out by interviews, observation and documentation, as well as using literature studies from various sources related to the questions asked in the research. It was found that there was a problem with the TPA Academic system, including regarding student registration, class division and scheduling, which still uses the bookkeeping method, it is difficult to get information regarding student data, class data, teacher data and subject data. Apart from that, the process of class division and scheduling cannot be completed quickly due to difficulties in finding data, the number of students being placed in excess of the quota often occurs, so mistakes or mistakes often occur in recording students' grades. So that registration, payment and distribution data can be processed quickly and accurately, a web-based application is needed. By implementing this application, it can help academic processing in preparing payment reports and student registration so that the management of TKA-TPA in the data collection process makes coordination and coaching easier. From the research results, it can be concluded that registration, payment, class division and scheduling using the bookkeeping method requires quite a long time in processing data and storing data.
Implementation of an Asset Management System Using the Straight-Line Method of Depreciation Based on Odoo 14 CE at PT Forecastle Indonesia Saputra, Hendra Ekky; Rasiban
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 3 (2024): DECEMBER 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i3.2975

Abstract

The purpose of this research is to implement an asset management system based on Odoo 14 Community Edition (CE) using a straight-line method at PT Forecastle Indonesia. Only the straight-line method is chosen as it gives the simple and efficient way to compute the depreciation of the asset over the useful life. Odoo 14 CE is selected for its rich features for asset management for tracking, depreciation calculation, maintenance, and reporting capabilities built in. The study consists of an analysis of the company needs, design based on straight-line method, Odoo 14 CE configuration, and observation and evaluates the implementation results. Key Outcomes: Increased efficiency in managing assets, accurate depredation tracking, reduced manual errors, better inter- department integration. The system is also expected to help prepare reports on assets-financial relations. We will then assess the implementation outputs against improvements in asset management efficiency and effectiveness (e.g. asset condition monitoring, maintenance costs management per asset, asset value tracking). The study will benefit the company by improving its operational and financial performance.
Sentiment Analysis of the Tapera Law on Platform X Using Naive Bayes Algorithm Bimantoro, Dava Sevtiandra; Rasiban
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 3 (2024): DECEMBER 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i3.3077

Abstract

The implementation of the 2016 Public Housing Savings Law (UU Tapera) aims to help legal and informal workers have decent houses through the management of housing savings funds by BP Tapera. However, when implemented, this program experienced obstacles amidst various problems including the transparency of the fund collection and management system, the unevenness of benefit provision, and variations in public perception. Sentiment analysis was conducted on Twitter data for sentiment regarding the Tapera Law to obtain public perception with Naïve Bayes. This approach classifies sentiment into positive, negative, and neutral. The accuracy of the Analysis Results was 62.47% (343 negative sentiments, 23 neutral, and finally 32 positive sentiments). The public mostly has negative sentiment towards the Tapera Law, because many of them are afraid of losing justice and effectiveness with this policy. These results underline the need to intensify transparency and communication of the benefits of the Tapera Law and its mechanisms to increase public acceptance and trust.
Analisis Sentimen Kepuasan Publik Terhadap Masa Kepemimpinan Shin Tae Yong Menggunakan Algoritma Naïve Bayes Nugraha, Pramudya; Rasiban; Sarimole, Frencis Matheos; Tundo
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 1 (2025): JANUARI-MARET 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i1.3020

Abstract

Shin Tae Yong is the coach of the Indonesian national team who has been a football player in South Korea and has coached the South Korean national team at the 2018 World Cup in Russia. Many people watch or pay attention to Shin Tae Yong's behavior and behavior when coaching the Indonesian national team. Shin Tae Yong has considerable worry with the Indonesian national team because of his strategy. However, there are several media that frame Shin Tae Yong's news differently so that differences in viewpoints and opinions on Shin Tae Yong are controversial, inviting many people to give their opinions. Therefore, people choose social media as a place to channel opinions. In this study, we will take tweets from X with search keywords for Shin Tae Yong and the Indonesian national team to process and classify the text using the sentiment analysis method. The text classification process is divided into two classes, namely positive sentiment classes and negative sentiment classes. The data used amounted to 2495 data that had been cleansed, which amounted to 2.348 Positive sentiment data and 147 data with negative sentiments so that they can be presented 98.94% positive and 60.00% negative, based on the classification of the Naïve Bayes algorithm model, using a split comparative data 0.8 :  0.2 With the value of k=3 for Shin Tae Yong's dataset, an accuracy value of 96.67%.
Co-Authors Ade Septiansyah Adnan, Kemal Agistia Yuliawati Amalia, Ghina Amir Tengku Ramly Arjun Fricco Arpinda, Arpinda Aryanti, Putri Gea Asep Maulana Aulia, Mutia Dwi Banase, Samuel Figo Beatrice Yrain Bening Sari Purnomo Bila, Septiyana Bimantoro, Dava Sevtiandra Boangmanalu, Raya Fitri Dadang Iskandar Mulyana` Evan Donaldo Febryan Bayu Pratama Feni Citra Dewi Firdaus, M. Aziz Hanif, Zuhdi Hermawan Susanto Hutagalung, Julinar Sari Ikha Novie Tri Lestari Imam Muftadi Julinar Sari Hutagalung Kurniawan Setyo Nugroho Megawati - Megawati Megawati Miftahul Jannah Ahyana Puteri Kharisha Muhammad Alfin Najib Muhammad Fakhri Pratama Muhammad Ilham Fadillah Muhammad Jardine Ramaddhani Mukminin, Mukminin Najib, Muhammad Alfin Nugraha, Pramudya Nunung Parawati Paidi, Imam Pradita, Anggi Puteri Kharisha, Miftahul Jannah Ahyana Putri Amira Sumitro PUTRI WULANDARI Rachmat Hidayat Insani Radikto Radikto Rahmah, Andini Raya Fitri Boangmanalu Rizal, Saepul Rizki Ananda Pratama Rudi Tri Jaya S Sutisna Samuel Praja Raymond Maruli Saputra, Hendra Ekky Sarimole, Frencis Matheos Sartika Mala Senika, Anis Septi Hasanah Setya Permana Sutisna SOPAN ADRIANTO Sri Lestari Sri Lestari Sugeng Riyadi Sumabrata, Raden Muhammad Jachfitrah Ardhi Suropati, Untung Sutisna Sutisna Sutisna Sutisna, Nandang Tanjung, Cici Yolanda Tri Agus Setiawan Tri Wahyudi Tri Wahyudi Tri Wahyudi Tri Wahyuni Triwahyudi, Triwahyudi Tundo, Tundo Untung Suropati Veren Nita Permatasari Wahab, Adnan Wahyu Saputro Wahyudi, Ahmad Arif Yansen Yansen Yuliansyah, Ahmad Fauzan Yusuf Pascal Ramadhan