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Applied Information Technology and Computer Science (AICOMS)
Published by Politeknik Negeri Ketapang
ISSN : -     EISSN : 29647703     DOI : https://doi.org/10.58466/aicoms
Core Subject : Science,
Computer Science & IT
Applied Information Technology and Computer Science (AICOMS) is an online version of national journal in Bahasa Indonesia and English, published by Department of Informatics Engineering, Politeknik Negeri Ketapang. AICOMS also has a print version. AICOMS also invites academics and researchers in the field of information technology, particularly from informatics engineering and information systems research to submit their articles. The articles to be published is an original work and has never been published. Incoming articles will be reviewed by a team of reviewers from internal and external sources.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 73 Documents
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Sistem Informasi Pemesanan Tiket Bus Menggunakan Algoritma Collaborative Filtering Berbasis Website Novtra Refiardiansyah Nasution; Aninda Muliani Harahap; Adnan Buyung Nasution
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/wnvsh708

Abstract

This study aims to design and develop a web-based bus ticket reservation information system that implements a Collaborative Filtering algorithm in a smart seat booking feature. The system was developed to address the limitations of manual ticket reservation processes, including restricted access to information, long queues, and low service efficiency. The research employed the Research and Development (R&D) method using the Waterfall model, which consisted of observation, interviews, literature review, system design, implementation, and testing. System evaluation was conducted through Unit Testing, System Testing, and User Acceptance Testing (UAT). The results indicate that the system successfully provides real-time departure schedule information, facilitates online ticket reservations, and generates seat recommendations based on user preferences. All major system functions achieved a 100% success rate during testing. UAT involving 30 respondents produced a user satisfaction score of 91%, while the Collaborative Filtering algorithm achieved a seat recommendation accuracy of 85%. The system also reduced ticket booking queue times by up to 70% compared with the previous manual process..
Implementasi Sistem Monitoring Smart Parking berbasis IoTmenggunakan LoRa dan SIM800L V2 Laily Muntasiroh; Akhmad Ulum; Sabhan Kanata
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/htmp4y72

Abstract

Parking area management in campus environments remains a significant challenge in the transformation toward an efficient and integrated smart campus ecosystem. This study proposes the implementation of an Internet of Things (IoT)-based smart parking monitoring system that integrates long-range communication (LoRa) and the GSM SIM800L V2 module within a two-node architecture to address the limitations of local network infrastructure. The primary objectives of this research are to: (1) evaluate the reliability of the system in detecting and transmitting parking slot status in real time, (2) measure the average transmission delay of data from sensor nodes to the server, and (3) assess the stability of data delivery under various GSM network conditions. The system was implemented using a combination of infrared sensors and ESP32 microcontrollers, while ThingsBoard was utilized as a web-based visualization platform. Experimental results demonstrated an average transmission delay of less than 3 seconds and a data transmission success rate exceeding 90%, even under marginal network conditions. These findings confirm the effectiveness of the LoRa-GSM-based IoT architecture for small-scale parking monitoring applications. Furthermore, the proposed system has the potential to be extended into a fully integrated intelligent parking solution supporting reservation services, occupancy prediction, 4G network utilization, and interoperability with other IoT platforms.
Analisis Sentimen Publik terhadap Isu Pembuatan CBDC di Indonesia Menggunakan IndoBERT Muhammad Radja Juang Jamemiko; Joseph Eduard Uly Loni; Muhammad Rizky Pribadi
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/srtytf27

Abstract

Perkembangan teknologi finansial mendorong munculnya inovasi sistem pembayaran digital, salah satunya melalui pengembangan Central Bank Digital Currency (CBDC) atau Rupiah Digital oleh Bank Indonesia. Penelitian ini bertujuan untuk menganalisis sentimen publik terhadap isu pembuatan CBDC di Indonesia berdasarkan opini masyarakat pada platform media sosial X. Penelitian menerapkan pendekatan Natural Language Processing menggunakan model Deep Learning berbasis Transformer, yaitu IndoBERT, untuk melakukan klasifikasi sentimen secara otomatis. Data tweet yang telah dikumpulkan melalui proses crawling kemudian melalui tahapan pre-processing, tokenisasi, serta klasifikasi ke dalam tiga kategori sentimen, yaitu positif, netral, dan negatif. Selain itu, penelitian juga melakukan visualisasi distribusi sentimen dan pemetaan kata dominan menggunakan wordcloud untuk mengidentifikasi fokus pembahasan masyarakat terkait CBDC ataupun Rupiah Digital. Hasil penelitian menunjukkan bahwa sentimen netral mendominasi diskusi publik sebanyak 61,01%, diikuti oleh sentimen negatif 29,11% dan positif 9,87%. Temuan ini mengindikasikan bahwa masyarakat masih berada pada tahap pengamatan dan diskusi terhadap implementasi CBDC, namun tetap terdapat kekhawatiran terkait aspek keamanan, privasi, dan kontrol sistem keuangan digital.
Prediksi Harga Saham Sektor Energi di BEI Menggunakan Model Multimodal LSTM dengan Integrasi Fitur Numerik dan Sentimen Berita Pasar Modal di Indonesia Naufal Lathifan Yumna; Ghufron Ghufron
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/jm8dtf93

Abstract

The movement of energy sector stock prices exhibits high volatility, which is influenced by historical data and news sentiment. This research evaluates the performance of stock price prediction by integrating numerical data and economic news texts using a multimodal Long Short-Term Memory (LSTM) architecture. The data utilized includes the stock prices of ADRO, PGAS, and INDY from the 2021 to 2026 period, alongside Indonesian-language economic news. Sentiment extraction from the news texts was conducted using the IndoBERT model. The results indicate that the IndoBERT model achieved an accuracy and F1-score of 83%. The evaluation of the unimodal model (historical data only) yielded a Mean Absolute Percentage Error (MAPE) of 3.62% for ADRO, 3.17% for PGAS, and 5.90% for INDY. Meanwhile, the multimodal model, which combined numerical and sentiment features, resulted in a MAPE of 4.00% (ADRO), 5.46% (PGAS), and 8.47% (INDY). In conclusion, the unimodal LSTM model proved to be effective; however, the integration of sentiment features in the multimodal scheme did not provide a significant improvement in accuracy due to the highly volatile nature of the stocks.
Analisis Sentimen Komentar Youtube terhadap Kondisi Bursa Saham Indonesia akibat Isu Pengunduran Serempak Dewan BEI Menggunakan IndoBERT Daffa Yudha Musyaffa; Felix Gunawan; Muhammad Rizky Pribadi
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/q27ea163

Abstract

Social media platforms such as YouTube have long served as a primary discussion space for retail investor communities in Indonesia. This study aims to analyze public sentiment in order to understand perception trends and the digital psychology of capital market participants regarding the issue of the simultaneous resignation of the Indonesia Stock Exchange (IDX) board members. The research applies the IndoBERT (Bidirectional Encoder Representations from Transformers for the Indonesian language) deep learning architecture through a fine-tuning process on a dataset of YouTube comments. The textual corpus was cleaned from noise, normalized from stock market slang vocabulary, tokenized, and automatically classified into three sentiment polarities: positive, neutral, and negative. The analysis stage was further continued with dominant keyword extraction using Word Cloud visualization and word frequency trend mapping to identify psychological variables driving market opinions. The model successfully classified the semantic complexity of informal language objectively. Visualization results indicate that communication dynamics were overwhelmingly dominated by negative sentiment (57.5%), reflecting widespread public concern and declining confidence in capital market stability due to the structural crisis. This study demonstrates the effectiveness of local transformer models as instruments for extracting digital market psychology to support real-time automated investment decision-making.
Sistem Informasi Kampus Politeknik Seruyan Berbasis Web untuk Mendukung Transformasi Digital Perguruan Tinggi Hermansyah Hermansyah; Rabiatul Rabiatul
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/zwetkz33

Abstract

Digital transformation in the higher education sector has become an important necessity in improving the quality of academic and administrative information services. Politeknik Seruyan, as a higher education institution, requires a web-based campus information system capable of providing fast, accurate, and integrated access to information for the entire academic community and the general public. This study aims to design and develop a web-based campus information system using the domain poltes.ac.id as the official campus information media. The website provides various important information including campus history, vision and mission, study programs, facilities and infrastructure, campus news, and other academic information services. The research method used is the Waterfall method consisting of requirements analysis, system design, implementation, testing, and maintenance stages. System implementation was carried out using the WordPress Content Management System (CMS) supported by a MySQL database and other web technologies to support website appearance and content management. The results of the study indicate that the developed campus information system is able to improve the effectiveness of information delivery, facilitate user access to data, and optimally support the digital transformation of higher education. The poltes.ac.id website also provides convenience for the public in obtaining official information related to Politeknik Seruyan online.
Analisis Sentimen Masyarakat terhadap Kenaikan Harga BBM Non-Subsidi Akibat Penutupan Selat Hormuz Menggunakan IndoBERT Jaysen Stephanus; Jonathan Tanujaya; Muhammad Rizky Pribadi
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/kx4xgz78

Abstract

Public discussions regarding the potential increase in non-subsidized fuel prices resulting from the closure of the Strait of Hormuz on the X platform between January 1, 2026, and May 17, 2026, were highly intensive and generated diverse public responses to the global economic impacts triggered by the geopolitical conflict between Iran and Israel. The primary issue addressed in this study is the growing public concern over the possibility of rising non-subsidized fuel prices, which may affect transportation costs, logistics distribution, and daily living expenses. This study aims to analyze public sentiment toward this issue using the IndoBERT deep learning model to obtain a more accurate understanding of public opinion trends. Data were collected through a scraping process on the X platform using keywords related to non-subsidized fuel and the Strait of Hormuz. The collected data were then processed through several preprocessing stages, including case folding, noise removal, tokenization, stopword removal, and stemming, before being classified into positive, neutral, and negative sentiment categories. Out of 412 analyzed tweets, negative sentiment emerged as the dominant category at 49.8%, followed by neutral sentiment at 48.5%, while positive sentiment accounted for only 1.7%. The findings indicate that the majority of the public expressed concern regarding the potential increase in non-subsidized fuel prices and its impact on economic conditions and household expenditures.
Perbandingan Metode Temporal Fusion Transformer (TFT) dan Long Short-Term Memory (LSTM) Dalam Prediksi Harga Saham Indonesia Berbasis Data Teknikal dan Fundamental Nurhasan Nurhasan; Ghufron Ghufron
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/jmhe8c20

Abstract

The Indonesian capital market exhibits a high level of volatility, requiring stock price prediction models that are accurate and adaptive. Conventional forecasting models have limitations in capturing nonlinear patterns and multivariate relationships within stock time series data. This study compares the performance of Long Short-Term Memory (LSTM) and Temporal Fusion Transformer (TFT) models in predicting stock prices of LQ45 index companies, namely BBRI, TLKM, and ADRO, with a 7-day forecasting horizon. Both models were trained using 12 combined technical and fundamental features with a data split ratio of 70:15:15. Model evaluation was conducted using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results indicate that the TFT model achieved better performance on BBRI with a MAPE of 0.74% and TLKM with a MAPE of 0.91%, while also demonstrating faster training convergence compared to LSTM. In contrast, the LSTM model outperformed TFT on ADRO with a MAPE of 2.71%, which exhibited a relatively consistent trend pattern. Overall, TFT proved to be more effective for stocks with complex multivariate dynamics, whereas LSTM remained competitive for stocks with more stable trend patterns. The selection of prediction models should therefore consider the volatility characteristics and movement patterns of each stock issuer
Pengembangan Sistem Informasi Pendaftaran Santri Berbasis Web Menggunakan Agile Scrum dan Evaluasi System Usability Scale (SUS) Mohamad Roikhan Makhmud; Endang Wahyuningsih
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/z8my8442

Abstract

The registration process for new students in many Islamic boarding schools is still done manually, causing efficiency constraints, service delays, and data management errors. This condition indicates that digital transformation in traditional Salaf Islamic boarding schools still faces challenges, especially related to user acceptance with varying levels of digital literacy. This study aims to develop a web-based student registration information system at Darussalam Adikarso Islamic Boarding School in Kebumen using the Agile Scrum approach and evaluate the system's usability using the System Usability Scale (SUS). The study used the Research and Development (R&D) method with Scrum stages including product backlog, sprint planning, sprint development, sprint review, and sprint retrospective. System testing was conducted using Black Box Testing, while the usability evaluation involved 21 respondents consisting of prospective students, guardians, and Islamic boarding school administrators. The results showed that all main features of the system run according to functional requirements. The usability test resulted in an average SUS score of 81.19 which is included in the Acceptable category with a grade A and an adjective rating of Excellent. The research findings show that the Agile Scrum approach is able to support the development of a system that is adaptive, easy to use, and in accordance with user needs in a traditional Salaf Islamic boarding school environment, thus potentially increasing the effectiveness and efficiency of student registration administration.
Analisis Sentimen menggunakan IndoBERT dan Tren Topik Keluhan Pasien pada Ulasan Google Maps Rumah Sakit Menggunakan Latent Dirichlet Allocation Naufal Muhammad Afif; Ghufron Ghufron
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/m86x0x75

Abstract

Patient satisfaction is a crucial indicator of hospital quality, yet management often focuses solely on star ratings that fail to explain the root causes of issues. This study develops a hybrid Natural Language Processing (NLP) model using IndoBERT for sentiment classification of Google Maps reviews. Reviews classified as negative sentiment are then filtered and processed using the Latent Dirichlet Allocation (LDA) method to uncover hidden themes within patient complaints. The test results show that the IndoBERT model achieves exceptionally high performance, with an accuracy of 95.23%, precision of 95.22%, recall of 95.23%, and an F1-score of 95.22%. The LDA analysis successfully identifies 10 optimal topics, which are categorized into five main complaint categories: time efficiency, medical services, facilities/parking, administrative procedures, and specialist services. The integration of IndoBERT and LDA proves effective in transforming raw digital reviews into strategic information for the automated evaluation of hospital service quality.

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