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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Advances in Data and Information Systems GANDRUNG: Jurnal Pengabdian Kepada Masyarakat The Indonesian Journal of Computer Science
Sofian Lusa
Fakultas Ilmu Komputer, Universitas Indonesia
Computer Science & IT Electrical & Electronics Engineering Engineering Physics Other

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Analisis Sentimen untuk Evaluasi Reputasi Merek Motor XYZ Berkaitan dengan Isu Rangka Motor di Twitter Menggunakan Pendekatan Machine Learning Ferdian Maulana Akbar; Robby Hermansyah; Sofian Lusa; Dana Indra Sensuse; Nadya Safitri; Damayanti Elisabeth
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 3: Juni 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.938663

Abstract

Motor XYZ mengeluarkan inovasi rangka motor yang diperkenalkan pada tahun 2019. Sekitar Agustus 2023, beredar rumor di media sosial yang menyatakan bahwa rangka tersebut mengalami karat, korosi, dan retak, menyebabkan kekhawatiran di kalangan masyarakat yang tentunya hal ini berpotensi merugikan reputasi merek XYZ. Sasaran utama dari studi ini adalah mengevaluasi pandangan masyarakat di platform Twitter pada Motor XYZ, khususnya pada perbincangan seputar isu rangka motor. Data yang digunakan merupakan data yang diambil teknik crawling dengan periode tweets dari Agustus hingga November 2023. Penelitian ini akan memanfaatkan analisis sentimen menggunakan word cloud, analisis tren dan distribusi, dan pembandingan lima algoritma machine learning, yakni Naïve Bayes, Decision Tree, Support Vector Machine, Logistic Regression, dan Random Forest. Penelitian ini bertujuan untuk mengidentifikasi algoritma dengan performa terbaik untuk mengategorikan tweets dan memberikan rekomendasi kepada Motor XYZ terkait reputasi merek dalam hubungannya dengan isu rangka motor. Hasil penelitian menunjukkan bahwa model klasifikasi sentimen dengan kinerja terbaik setelah hyperparameter tuning adalah Random Forest, dengan F1 score sebesar 0,765. Selain itu, rekomendasi yang dapat diberikan adalah meningkatkan kesadaran tentang pemeriksaan rangka gratis karena telah terbukti berdampak positif pada sentimen masyarakat di Twitter. Perlu ditekankan bahwa dalam penelitian ini tidak ada pertimbangan terhadap proses deployment model machine learning dan pembuatan dashboard. Selain itu, penelitian ini tidak menangani analisis reputasi atau sentimen merek di platform media sosial lain seperti TikTok atau Instagram.   Abstract Motor XYZ introduced an innovative motorcycle frame in 2019. In August 2023, rumors began circulating on social media that these frames were experiencing rust, corrosion, and cracks. This caused public concern and potentially harmed the XYZ brand's reputation. This study aims to evaluate public opinion on Twitter regarding the motorcycle frame issue. Data was collected using crawling techniques from tweets posted between August and November 2023. We used sentiment analysis with word clouds, trend and distribution analysis, and compared five machine learning algorithms: Naïve Bayes, Decision Tree, Support Vector Machine, Logistic Regression, and Random Forest. The goal was to identify the best algorithm for categorizing tweets and provide recommendations to Motor XYZ about their brand reputation concerning the frame issue. Results showed that the Random Forest model, after hyperparameter tuning, had the best performance with an F1 score of 0.765. This study recommend increasing awareness about free frame inspections, as this positively impacted public sentiment on Twitter. Note that this study does not include the deployment process of the machine learning model or dashboard creation, nor does it address brand reputation or sentiment analysis on other social media platforms such as TikTok or Instagram.
Melihat lebih lanjut Knowledge Sharing pada Industri Pengembangan Perangkat Lunak: Sebuah Systematic Literature Review terhadap Faktor-Faktor yang mempengaruhi Knowledge Sharing dan Implementasi Praktisnya Ariq Naufal Satria; Ringgi Cahyo Dwiputra; Dana Indra Sensuse; Sofian Lusa; Damayanti Elisabeth
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.v13i4.4306

Abstract

In an organizational context, effective knowledge sharing can provide significant benefits to the organization. Efficient knowledge sharing can enhance the quality of individuals, thereby automatically improving the overall quality and performance of the organization. This process is particularly suitable for companies engaged in activities requiring complex knowledge, such as software development. However, to implement effective knowledge sharing, a company must possess strong internal capabilities. Furthermore, the forms of knowledge sharing implementation can vary depending on the organization. This research aims to identify the influencing factors of knowledge sharing along with their practical implementations within software development in general using systematic literature review. Several studies were obtained to answer the research questions. The results reveal those 40 distinct factors impacting knowledge sharing behavior individual, organizational, and technological aspects. Additionally, 16 different implementations of knowledge sharing were discovered, with knowledge repositories and meetings as the most common practices.
Designing a Knowledge-Based Chatbot to Elevate Business Licensing Services in Indonesia Husain, Husain; Ridwan Afandi; Dana Indra Sensuse; Sofian Lusa; Nadya Safitri; Damayanti Elisabeth
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 5 (2024): October 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i5.6069

Abstract

The business licensing process in Indonesia often faces several challenges, including lack of information, unstable system, complicated procedure, and slow response to complain. These issues can hinder economic growth and limit access for businesses. This research aims to design a knowledge-based chatbot to elevate business licensing services in Indonesia. The proposed chatbot will utilize natural language processing (NLP) technology and a structured knowledge base to provide accurate information, assist in form filling, and offer step-by-step guidance to users. This research employes a User-Centered Design (UCD) approach to ensure that the developed chatbot meets the needs and preferences of its users. The research stages involve user requirements analysis, UML design, system design, and iterations based on feedback obtained. Data will be collected through questionnaires, interviews, and literature studies. Leveraging the proposed architecture, we demonstrate how the resulting knowledge-based chatbot is expected to enhance business licensing services. The findings identified 8 key features expected in the chatbot, including real-time information access, problem reporting, business licensing guidance, a tracking system, personalized simulation, a feedback mechanism, multilingual support, and the ability to connect with a contact center agent. By implementing these features, the proposed chatbot is anticipated to significantly reduce processing times, streamline user interactions, and enhance user satisfaction by providing real-time assistance and reducing errors in form submissions. This will contribute to a more efficient licensing process, fostering economic growth and improving the business environment in Indonesia.
Strategi Manajemen Pengetahuan untuk Mengoptimalkan Operasi Help Desk: Tinjauan Literatur Sistematis Lathiful Alamsyah; Fachri Munandar; Dana Indra Sensuse; Sofian Lusa; Nadya Safitri; Damayanti Elisabeth
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): 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.v14i1.4648

Abstract

In today's digital era, effective knowledge management (KM) is vital for businesses, especially in public sector help desks where user interaction is critical. This systematic literature review (SLR) explores factors influencing KM implementation and identifies strategies to optimize help desk operations. Using PRISMA criteria and the PICO model, 25 studies from 2019 to 2024 were selected after screening 5,490 publications. Key factors impacting KM include organizational culture, leadership support, and technological infrastructure. Recommended strategies involve fostering a knowledge-sharing culture, developing knowledge bases, and utilizing AI for knowledge capture. The findings contribute theoretically by consolidating a framework for KM in help desks and practically by guiding public sector organizations. However, reliance on secondary data limits the study, as it may not fully reflect real-world KM practices. Future research could empirically validate these findings and explore emerging technologies like AI to enhance KM effectiveness.
Knowledge Management Foundation and Solutions Implementation in Indonesian Government Higher Educational Institution Sihombing, Boy Sandi Kristian; Fatoumatta Binta Jallow; Ghina Fitriya; Dana Indra Sensuse; Sofian Lusa; Damayanti Elisabeth; Nadya Safitri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.6005

Abstract

The performance of XYZ, a Government Higher Educational Institution (GHEI) in Indonesia is assessed through two unintegrated applications. The 2023 target performance was missed due to miscalculations outside applications while transforming large data amounts. Thus, business intelligence (BI) serves as a knowledge management (KM) tool to integrate those applications to achieve XYZ's target. Because BI is costly and has a 70% failure rate of development plans, a research model was evaluated to look at the current XYZ innovation capability for successful BI adoption from the KM foundation and KM solution implementation. This study used a quantitative method, employing a questionnaire for 94 civil servants and the partial least squares-structural equation model (PLS-SEM) for data analysis. Results indicate in the KM foundation, organizational (O) negatively influences KM process application (KMP) (β = -0.292, Pv = 0.010) while KM infrastructure (I) and process (P) positively influence KMP, but KM technology (T) does not. In KM solutions, KMP is proven to be linked to innovation capability when KM systems are lacking. Hence, several activities are suggested to activate T through T, O, P, and I. The model validated 80% of the hypotheses, laying the groundwork for future studies into which aspects of T strengthen innovation capabilities in GHEI.
Understanding User Acceptance towards Case Management System: A UTAUT Analysis Tambunan, Ester Marta; Sensuse, Dana Indra; Sofian Lusa
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.v13i4.3930

Abstract

To improve the quality of case handling administration, the Indonesian National Police (Polri) has implemented a technology-based platform, a Case Management System (CMS). This study employs the UTAUT model to examine the factors influencing the interest of Polri investigators in using the CMS. Data were collected through an online survey distributed to all investigators within the Polri. Multivariate analysis using PLS-SEM was conducted to identify and predict the factors significantly influencing user acceptance. The results indicate that government policies and regulations, performance expectancy, and social influence significantly influence behavioral intentions towards the system, whereas effort expectancy does not. Behavioral intentions and facilitating conditions significantly influence system usage behavior. In conclusion, the UTAUT model demonstrates valid and reliable measurements, as well as a good goodness of fit in predicting investigators’ interest or intention to use the CMS system, which can be utilized for the development of information systems in government agencies.
Faktor-faktor yang Memengaruhi Perilaku Knowledge Sharing di Kalangan Software Developer di Indonesia Ringgi Cahyo Dwiputra; Ariq Naufal Satria; Dana Indra Sensuse; Sofian Lusa; Damayanti Elisabeth
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.v13i4.4125

Abstract

Knowledge sharing is a crucial element for enhancing efficiency in the software development process. However, it proves to be a challenging and complicated task in practice, particularly due to the insufficient knowledge and experience of software developers. The aim of this research is to pinpoint key success factors in knowledge sharing behavior among software developers. Based on Social Cognitive Theory (SCT), the research divides components into three categories: behavioral, environmental, and personal. For a more complete picture, an additional organizational aspect is included. The partial least squares structural equation model was utilized to analyze the data collected from 198 software developers in Indonesia. The findings reveal that motivation, trust, social interaction, organizational culture, reward, and management support positively influence knowledge sharing behavior, while geographical distance has a negative impact. This research contributes by filling a gap in previous research that utilized SCT, broadening the model to identify determinant factors explaining knowledge sharing behavior within an organizational context.
Development of A Thematic Map of Tourism Potential in Ogan Ilir District, South Sumatra Myrza Rahmanita; Surya Fadjar Boediman; Sri Mariati; Sofian Lusa
GANDRUNG: Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 1 (2025): GANDRUNG: Jurnal Pengabdian Kepada Masyarakat
Publisher : Fakultas Olahraga dan Kesehatan, Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/gandrung.v6i1.4463

Abstract

Community service activities (PKM) carried out in Ogan Ilir Regency were initiated as an answer to the needs of the local community to develop tourism potential by empowering the community through skills training and counseling. The main objective of this PKM is to increase the income and quality of life of local communities by introducing new techniques in tourism management and other supporting skills. The implementation method consists of four stages: first, initial meetings with local governments to explain objectives and build cooperation; second, field survey to collect data through observation and interviews; third, data analysis to compile a thematic map of tourism potential; fourth, Forum Group Discussion (FGD) to present the results and obtain input. The results of these activities show a significant increase in community knowledge and skills regarding tourism management. The community is now more ready to take advantage of local tourism potential which is expected to increase their income and quality of life. Implementation of recommendations from PKM is expected to make a positive contribution to tourism development and community welfare in Ogan Ilir Regency.
AI Agents for Organizational Knowledge Retrieval and Sharing: A Systematic Literature Review Sri Rosa Anjelia; Dana Indra Sensuse; Sofian Lusa
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i3.1462

Abstract

As complexity and volume of data continue to increase, studies have found that traditional knowledge management systems are unable to keep up. Distributed teams, which are increasingly adopted by organizations as ways of working, have significantly transformed how employees manage their knowledge within these organizations. Artificial intelligence (AI), especially AI agents, is increasingly being used by organizations as a solution to enhance knowledge retrieval and sharing. However, it remains fragmented, with little awareness of its effective capabilities, limitations, and implications for organizational knowledge processes. The objective of this study was to systematically evaluate and synthesize recent research on AI agents for organizational knowledge retrieval and sharing. A PRISMA-based Systematic Literature Review (SLR) was carried out on studies published between 2021 and 2025. A total of 28 studies were analyzed to classify AI agent capabilities, supporting technologies, and key challenges across diverse domains and regions. The results revealed five key capabilities of AI agents, such as user-centered interaction, semantic knowledge extraction & retrieval, intelligent reasoning & decision support, automation & workflow management, and explainability & traceability. These capabilities were supported by technologies such as large language models, machine learning, natural language processing, knowledge graphs, ontologies, and other prominent technologies. Adoption challenges primarily included data quality & semantic alignment, system interoperability, trust & adoption issues, and ethical & governance concerns. This review concludes that AI agents hold strong potential to improve organizational knowledge processes, but it requires strategic integration, strong data governance, and human-centered design principles.
Co-Authors Ariq Naufal Satria Boediman, Surya Fadjar Damayanti Elisabeth Dana Indra Sensuse Fachri Munandar Fatoumatta Binta Jallow Ferdian Maulana Akbar Ghina Fitriya Husain Husain Lathiful Alamsyah Myrza Rahmanita Nadya Safitri Ridwan Afandi Ringgi Cahyo Dwiputra Robby Hermansyah Sihombing, Boy Sandi Kristian Sri Mariati Sri Rosa Anjelia Tambunan, Ester Marta