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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Bulletin of Electrical Engineering and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Informasi dan Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Tedc Jurnal Sistem Informasi The Indonesian Journal of Computer Science

Indra Budi

Fakultas Ilmu Komputer Universitas Indonesia

Author-ID : 286746

Religion Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Veterinary Other

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Klasifikasi Ulasan Pengguna Aplikasi: Studi Kasus Aplikasi Ipusnas Perpustakaan Nasional Republik Indonesia (PNRI) Andina Septiani; Indra Budi
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 7, No 4 (2022)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v7i4.3216

Menurunnya jumlah tren pengguna baru aplikasi iPusnas berpengaruh terhadap penurunan pencapaian nilai target laporan LKIP Pujasintara PNRI 2020 – 2024. Hal tersebut berkaitan dengan nilai peringkat ulasan pengguna aplikasi di Google Playstore yang dinilai masih lebih rendah dibandingkan aplikasi sejenis lainnya. Electronic Word of Mouth (EWOM) yang sangat berpengaruh terhadap keputusan calon pengguna baru aplikasi dalam mempertimbangkan aplikasi terbaik yang sejenis, karena melibatkan tinjauan nilai peringkat dan ulasan pengguna. Beberapa penelitian terdahulu membuktikan bahwa kesulitan selalu dihadapi ketika melakukan analisis atau penggalian informasi penting dalam ulasan pengguna aplikasi secara manual. Analisis ulasan sangat berguna untuk mengembangkan fitur layanan aplikasi agar dapat meningkatkan kepuasan pengguna dan peringkat nilai aplikasi, sehingga diperlukan alat bantu klasifikasi ulasan pengguna secara otomatis dengan mencari model terbaik yang sesuai. Penelitian ini menerapkan metodologi CRISP-DM, tetapi hanya sampai tahap evaluasi. Algoritma klasifikasi yang digunakan adalah Naïve Bayes, Logistic Regression, Support Vector Machine (SVM), serta kombinasi fitur tf-idf unigram, bigram, dan trigram. Hasil penelitiannya adalah kombinasi fitur tf-idf unigram (F1) dengan algoritma SVM mencapai nilai terbaik untuk setiap nilai evaluasi precision, recall, dan f1-score masing-masing sebesar 87%. Nilai evaluasi terendah precision 55% dari hasil kombinasi fitur F2 dengan SVM, recall 42% dan f1-score 32% dari kombinasi fitur F3 dengan logistic regression.

Application of named entity recognition method for Indonesian datasets: a review Indra Budi; Ryan Randy Suryono
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4529

A name entity (NE) is a proper name that designates a person, location, or organization. For humans, named entity recognition (NER) is a straightforward process insofar as many named entities are self-names, and most of them have initial capital letters and can be easily recognized, but it is very difficult for machines. This study discusses research trends in the application of NER to Indonesian datasets, particularly as it concerns certain tasks, datasets, methods/techniques, and entity labels. By conducting a systematic literature review (SLR) and bibliometric analysis with VOSviewer, this article hopes to provide opportunities for adopting old methods, combining models from previous research, and even proposing new methods. In addition, the motivation for doing SLR at NER is to look for new strategies in the supervision of financial technology (Fintech). If machines can find illegal Fintech entities on social media and online news, it can help the government to block these illegal Fintech entities. To this end, this study provides an overview of research trends in applying the NER method to Bahasa Indonesia (Indonesian) datasets, including the extraction of news articles, the monitoring of floods, and traffic.

KERANGKA KERJA PEMILIHAN FREE AND OPEN SOURCE SOFTWARE (FOSS) MENGGUNAKAN METODE ANALYTICAL HIERARCHY PROCESS Ario Santoso; Indra Budi
Jurnal Sistem Informasi Vol. 4 No. 2 (2008): Jurnal Sistem Informasi (Journal of Information System)
Publisher : Faculty of Computer Science Universitas Indonesia

Banyaknya pilihan Free Open Source Software (FOSS) dengan beraneka ragam kualitas menyulitkan kita memilih FOSS yang tepat untuk memenuhi kebutuhan kita. Oleh karena itu, dibutuhkan suatu metode untuk membantu dalam memilih FOSS yang tepat. Paper ini bertujuan untuk mengemukakan suatu kerangka kerja (framework) yang dapat memberikan tuntunan atau bantuan dalam memilih FOSS. Kerangka kerja yang diajukan ini menggunakan metode Analytical Hierarchy Process untuk membantu proses pemilihan solusi.

PETA RENCANA (ROADMAP) RISET ENTERPRISE RESOURCE PLANNING (ERP) DENGAN FOKUS RISET PADA USAHA KECIL DAN MENENGAH (UKM) DI INDONESIA Putu Wuri Handayani; J.W. Saputro; Achmad Nizar Hidayanto; Indra Budi
Jurnal Sistem Informasi Vol. 6 No. 2 (2010): Jurnal Sistem Informasi (Journal of Information System)
Publisher : Faculty of Computer Science Universitas Indonesia

Usaha Kecil dan Menengah (UKM) di Indonesia telah dapat berkontribusi terhadap PDB (Produk Domestik Bruto) nasional sebesar 55.56% berdasarkan data Biro Perencanaan Kementerian Negara Koperasi dan UKM Republik Indonesia, pada tahun 2008. Untuk memperluas pangsa pasar dan meningkatkan daya saing UKM, UKM membutuhkan suatu aplikasi yang dapat mengintegrasikan dan mengotomatisasi proses bisnis UKM. Aplikasi ERP dapat menjadi salah satu solusi untuk UKM dikarenakan keuntungan yang dapat diberikan seperti memberikan informasi dengan waktu respon yang cepat, meningkatkan interaksi antar bagian dalam suatu organisasi, meningkatkan pengelolaan siklus pemesanan barang, dsb. Beberapa isu kritis yang dihadapi oleh UKM adalah terbatasnya dana dan kapabilitas teknologi informasi yang dimiliki. Dalam memahami kebutuhan layanan yang diperlukan oleh UKM untuk aplikasi ERP dan untuk menyediakan arahan bagi UKM serta menanggapi kurangnya riset ERP di Indonesia maka riset ini bertujuan untuk menggambarkan peta rencana jangka panjang dari agenda riset ERP yang akan dilakukan untuk UKM di Indonesia. Small and Medium Enterprises (SMEs) in Indonesia has been able to contribute to the GDP (Gross Domestic Product) of 55.56% based on national data Planning Bureau of the Ministry of Cooperatives and SMEs of the Republic of Indonesia, in 2008. To expand market share and improve the competitiveness of SMEs, SMEs need an application that can integrate and automate business processes of SMEs. ERP applications can be one solution for SMEs because of the advantages that can be provided such as providing information with fast response time, increase the interaction between the departments of an organization, improving the management of ordering goods cycle, etc. Some of the critical issues faced by SMEs are the limited funds and information technology capabilities they have. In understanding the needs of the services required by SMEs for ERP applications and to provide guidance for SMEs and response to the lack of research about ERP in Indonesia, this research aims to describe the long-term plan maps of the ERP's research agenda that will be made for SMEs in Indonesia.

ANALISIS PENGUKURAN TINGKAT KESIAPAN PENERAPAN MANAJEMEN PENGETAHUAN: STUDI KASUS BADAN PENDIDIKAN DAN PELATIHAN KEUANGAN, KEMENTERIAN KEUANGAN Hafid Mukhlasin; Indra Budi
Jurnal Sistem Informasi Vol. 13 No. 1 (2017): Jurnal Sistem Informasi (Journal of Information System)
Publisher : Faculty of Computer Science Universitas Indonesia

One strategy of the Ministry of Finance in the institutional transformation is to strengthen the role of Financial Education and Training Agency (FETA) in the development of human resources is to become a corporate university. Furthermore, base on theory that one of the important elements that should exist in an organization to become a corporate university is knowledge management (KM). The problem which occurs in FETA is that the organization does not have specific mechanisms to manage the knowledge. Moreover, to overcome that problem, the organization need to build a KM system that allows each individual to share useful knowledge for the organization. As the first step, it needs a good preparation to lessen the failure in implementing it; which is by measuring the readiness level of knowledge management implementation. Therefore, this study aims to measure the readiness level of knowledge management implementation in FETA in order to provide recommendations for improving the readiness of it. The readiness level is measured based on the variables mapping including KM Infrastructure, KM Enabler, and KM Critical Success Factor and then mapped into the KM aspects. The data were collected by using a sample survey method which were gathered from the employees of FETA. The data are then analyzed descriptively and inferentially to get the description about the readiness level of FETA in implementing knowledge management. Based on this research, it can be concluded that FETA is at the receptive level of readiness in implementing knowledge management. It indicates that all the indicators in KM, have been very supportive to the implementation of KM in FETA.

SEGMENTASI PELANGGAN PADA CUSTOMER RELATIONSHIP MANAGEMENT DI PERUSAHAAN RITEL: STUDI KASUS PT GRAMEDIA ASRI MEDIA Christina Deni Rumiarti; Indra Budi
Jurnal Sistem Informasi Vol. 13 No. 1 (2017): Jurnal Sistem Informasi (Journal of Information System)
Publisher : Faculty of Computer Science Universitas Indonesia

Advances in information technology produces wide range of choices in accessing information including reading books. The increase in the number of readers who turning to electronic books making sales of printed books has decreased in the recent years. PT Gramedia Asri Media is one of book retail company in Indonesia. Gramedia implement CRM by launching a member card named Kompas Gramedia Value Card (KGVC). Promotion given has not been able to increase book transaction of KGVC members.This research focus on make customer segmentation in CRM at PT Gramedia Asri Media. Data mining process is done by clustering using K-means algorithm for segmenting customers based on RFM, as well as hierarchical clustering algorithms for segmentation of customers based on the number of books type. Evaluation is done on cluster result using elbow method, silhouette method, and Calinski-Harabasz index. Customer segmentation based on the RFM produce two optimal clusters, occasional customers and dormant customers. Customer segmentation based on the number of types of books purchased produce 3 optimal cluster, namely low, medium, and high. With these results, it is expected to help the company classifying KGVC members to determine the appropriate strategies, so company can increase the number of books transactions.

Stance Analysis of Policies Related to Emission Test Obligations using Twitter Social Media Data Dwi Retnoningrum; Dea Annisayanti Putri; Indra Budi; Aris Budi Santoso; Prabu Kresna Putra
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 3 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i3.69004

Social media is currently widely used to disseminate various kinds of information, whether expressing feelings, or opinions. Public opinion is no exception regarding government policies and the implementation of emission tests, which describe the conditions that exist in society. Information on public opinion data obtained through social media in real time can assist the government in evaluating policies and improving the quality of currently implemented policies, particularly evaluating the implementation of emission tests on motorized vehicles. In this research, the application of stance analysis is used to evaluate emission test policies based on public opinion.In addition, this research aims to combine several machine learning methods and feature extraction methods to find the best combination based on accuracy, training time, and prediction time based on emission test policies. The best model based on the level of accuracy is a combination of Decision Tree and BERT, which reaches a value of 66%. Meanwhile, based on training time, the model that has the advantage is the Ridge Classifier with fasttext text representation. Based on prediction time, there are 3 combination models, namely Decision Tree with word2vec, SVM with Word2Vec, and Logistic Regression with fasttext text representation.

Incorporating Stock Prices and Social Media Sentiment for Stock Market Prediction: A Case of Indonesian Banking Company Dhenda Rizky Pradiptyo; Irfanda Husni Sahid; Indra Budi; Aris Budi Santoso; Prabu Kresna Putra
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.74486

Forecasting the stock market is one of the most popular topics to be discussed in many fields. Many studies, especially in information technology have been conducted machine learning algorithms to achieve a more accurate prediction of the stock market. This research aims to find the effectiveness in predicting stock market performance by utilizing social media sentiment in combination with historical data. In addition, this research uses a machine learning algorithm to train a model to predict the stock price of each bank and training the model on a dataset that included the historical stock prices of the bank, as well as the sentiment scores of the social media posts about the bank and evaluate the performance of the model by comparing the predicted stock prices to the actual stock prices. The research shows that the R2 and RMSE score model that has been built with its historical data has slightly better performance than the model that has been built with the combination of historical data and social media sentiment. The finding indicates that the research method is closely correlated and affected to the performance of the stock market prediction.

Klasifikasi Ticket Service Desk Perusahaan Asuransi Jiwa Berbasis Machine Learning Imbenay, Joash Lorenzo; Indra Budi
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4142

This study focuses on developing a ticket classification model for the Service Desk at an insurance company to enhance operational efficiency. Manual ticket classification is time-consuming and prone to errors, so the research aims to compare the performance of various classification algorithms to determine the best model. The methodology involves text mining and machine learning techniques using four main algorithms: Random Forest, Decision Tree, Support Vector Machine (SVM), and Naïve Bayes. The data comes from Service Desk tickets processed through text preprocessing stages. Findings indicate that the Random Forest model with a combination of TF-IDF Unigram features in the Access context performs best in classifying IT Support tickets, with a Precision of 0.76%, Recall of 0.66%, F-Score of 0.70%, and Accuracy of 0.54%. Implementing this model is expected to improve operational efficiency and user satisfaction with IT services, speeding up ticket handling, reducing administrative workload, and enhancing user satisfaction with IT services.

Twitter Sentiment Analysis Towards Candidates of the 2024 Indonesian Presidential Election Cahyanti, Rhoma; Desiana Nurul Maftuhah; Aris Budi Santoso; Indra Budi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5839

Long before the elections were held, the topic related to elections was widely discussed on news portals and social media, including Twitter. A few studies related to the Indonesian election have tried to predict candidates who will run for the presidential election, but there has been no research that examines public sentiment on social media towards each of the potential candidates. The main objective of this study is to analyze the public sentiment in Twitter towards potential candidates for the 2024 Indonesian presidential election. This research seeks to fill the gaps in previous research and become a reference for further research regarding sentiment analysis for election prediction using Twitter. The presidential candidates used in the research are the top 3 candidates based on the Poltracking survey, namely Ganjar Pranowo, Prabowo Subianto, and Anies Baswedan. The data were taken from January until October 2022, more than a year before the general election began. To predict the sentiment, four different machine-learning methods were used and compared to each other. There are Naïve Bayes, Support Vector Machines, Random Forests, and Neural Networks. Based on the sentiment results of each candidate, the highest sentiment towards Prabowo is neutral (55.49%), the highest sentiment towards Ganjar is positive (61.34%), and the highest sentiment towards Anies is neutral (44.84%). Results from the study also show that Anies was the presidential candidate who received more negative sentiment than the other two (56.63%). Meanwhile, Ganjar Pranowo got the most positive sentiment of all (42,69%). For the neutral sentiment, Anies Baswedan also got the most results (39,87%), followed by Prabowo (38.99%) and Ganjar Pranowo (21.14%). The result of the study also discovered that random forest and neural networks have the best performance for sentiment analysis.

Co-Authors Achmad Nizar Hidayanto Adimas Fiqri Ramdhansya Agus Widodo Agus Widodo Andina Septiani Ari Sudrajat Ario Santoso Ario Santoso Ario Santoso Aris Budi Santoso Aris Budi Santoso Astria Kurniawan Sumantri Cahyanti, Rhoma Chandra Lesmana, Chandra Chandra Sukma Christina Deni Rumiarti Dea Annisayanti Putri Desiana Nurul Maftuhah Dhenda Rizky Pradiptyo Dian Adiputro Dwi Retnoningrum Hafid Mukhlasin Handayani, Putu Wuri Heri Kurniawan Imbenay, Joash Lorenzo Irfanda Husni Sahid J.W. Saputro J.W. Saputro J.W. Saputro Muhammad Okky Ibrohim Mukhlasin, Hafid Novita Lestari Anggreini Prabu Kresna Putra Prabu Kresna Putra Rizal Fathoni Aji Rizal Fathoni Aji Rizal Mulyadi Rumiarti, Christina Deni Ryan Randy Suryono Shella Maria Vernanda Sri Widiastuti

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