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INDONESIA
INTI Nusa Mandiri
Published by PPPM Nusa Mandiri
ISSN : 02166933     EISSN : 2685807X     DOI : -
Core Subject : Science,
Computer Science & IT
The INTI Nusa Mandiri Journal is intended as a media for scientific studies on the results of research, thought and analysis-critical studies on the issues of Computer Science, Information Systems and Information Technology, both nationally and internationally. The scientific article in question is in the form of theoretical review and empirical studies of related sciences, which can be accounted for and disseminated nationally and internationally.
Arjuna Subject : -
Articles 475 Documents
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ALGORITHMIC TRADING DENGAN STRATEGI MENGIKUTI TREN MENGGUNAKAN EXPONENTIAL MOVING AVERAGE DAN STOCHASTIC OSCILLATOR Ahmad Juniar; Asep Juarna
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8654

Abstract

The restrictions on social interactions during the COVID-19 pandemic encouraged individuals to seek alternative sources of income. Advances in information technology have enabled investors to participate in financial markets through online platforms. According to data from the Indonesian Central Securities Depository (KSEI), the number of capital market investors in Indonesia increased between 2000 and 2022. Algorithmic trading refers to a systematic trading approach in which computer algorithms are utilized to generate trading signals and execute market orders automatically according to predefined trading rules. This approach employs technical analysis to forecast asset or commodity prices using historical price and trading volume data. This study develops an algorithmic trading system for cocoa futures, a commodity traded on the Intercontinental Exchange (ICE) New York and ICE London. The proposed model incorporates two technical indicators, namely the Exponential Moving Average (EMA) and the Stochastic Oscillator. The trading system was implemented using the MQL5 programming language on the MetaTrader 5 platform. Historical cocoa price data covering the period from 2024 to 2025 were obtained from Dukascopy Bank SA, a Swiss banking institution. The experimental results show that the proposed system achieved a win rate of 42.59% and a profit factor of 1.63, while maintaining a maximum drawdown of 8.52%, indicating a relatively low level of risk. Furthermore, the equity curve exhibited consistent growth and the histogram analysis demonstrated stable performance across most trading days. Based on established performance metrics, the proposed system is considered viable and sufficiently robust, as it successfully withstands multiple market cycles.
DETEKSI SPOILER PADA ULASAN BUKU BERBAHASA INDONESIA MENGGUNAKAN PENDEKATAN MACHINE LEARNING DAN DEEP LEARNING Natasya Agustine Sadhi; Hannah Larissa Halim; Jessica Winola; Viny Christanti Mawardi
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8587

Abstract

Spoiler detection in book reviews is a challenging text classification task because spoilers are not always identifiable from specific words but depend heavily on the narrative context in which information is revealed. This study compares six text classification models for detecting spoilers in Indonesian-language book reviews from Goodreads, namely Support Vector Machine (SVM), Random Forest, XGBoost, Bidirectional Long Short-Term Memory (BiLSTM), IndoBERT, and XLM-R (RoBERTa). Data were collected through Selenium-based web scraping and GraphQL API from 40 mystery and thriller book titles, resulting in 11,259 reviews with a class imbalance ratio of 1:10.4. All models were evaluated using AUC-ROC, PR-AUC, spoiler F1-score, and spoiler recall as primary metrics, with decision thresholds optimized through each model's validation set. XLM-R achieved the best overall performance with an AUC-ROC of 0.7017, a PR-AUC of 0.2263, and a spoiler F1-score of 0.2903, followed by IndoBERT, SVM, Random Forest, XGBoost, and BiLSTM. Overall, transformer-based models outperformed the traditional machine learning models and BiLSTM across most evaluation metrics. The results also indicate that each model exhibits different precision-recall characteristics, suggesting that model performance should not be evaluated using a single metric alone. These findings can serve as an initial reference for future research on Indonesian-language spoiler detection and support the development of automated spoiler detection systems for content moderation on digital book review platforms
INVESTIGASI HATE SPEECH PADA PLATFORM "X" MENGGUNAKAN METODE HYBRID INDOBERT–GRAPH ATTENTION NETWORK Weslie Austin; Puguh Hiskiawan
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8699

Abstract

The rapid growth of social media, particularly the X (formerly Twitter) platform, has made it a primary space for public discourse in Indonesia, yet its openness has also become a systematic loophole for the spread of hate speech that threatens social cohesion. Conventional text-based detection models fail to capture hidden linguistic nuances such as slang and regional euphemisms, and they disregard the social dimension of hate propagation reflected in user interaction patterns. This study proposes a hybrid IndoBERT-GAT architecture that integrates textual semantic representation with social graph structure into a single unified framework. The research method employed 16,646 government-related tweets collected via Apify crawling, labeled through a hybrid approach (manual annotation and pseudo-labeling), then represented through IndoBERT embeddings for textual features and a Graph Attention Network to model a heterogeneous tweet-user graph, before being combined via a feature fusion mechanism and evaluated through an ablation study across four model scenarios. The results show that the full Hybrid IndoBERT-GAT model achieved the highest test F1-score (65%), outperforming the same architecture without metadata (61.8%), the GAT+metadata-only baseline (42.2%), and the MLP+metadata baseline (39.9%), demonstrating that IndoBERT's semantic representation is the dominant contributor while graph structure and metadata serve as complementary signals, although the model still tends to overpredict hate speech in politically sarcastic content that uses sharp language without genuinely hateful intent.
PENGAMANAN DATA PELANGGAN SISTEM INFORMASI PROVIDER WIFI MENGGUNAKAN ALGORITMA AES-128 MODE CIPHER BLOCK CHAINING Muhammad Andara Ghazy Ar Ramadhan; Ismail Abdurrozzaq Zulkarnain; Adi Fajaryanto Cobantoro
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8720

Abstract

The increasing use of web-based information systems by Internet Service Providers (ISPs) has increased the need to protect customers' personal data from unauthorized access and misuse. This study aims to implement the Advanced Encryption Standard (AES-128) algorithm in Cipher Block Chaining (CBC) mode to improve customer data security in a web-based WiFi provider information system. The system was developed using the Waterfall method, including requirement analysis, system design, implementation, testing, and maintenance. Customer data were encrypted before being stored in the database and decrypted when users with access rights can view it. System validation was conducted using White Box Testing with the Path Testing technique. Based on the test results, the plaintext "ANDARA1234567890" can be processed into the ciphertext "539E82687FC3424ADA78E95A877AE37E7DE45E3AEA0B76E97D26D3C15152E74B" using the AES-128 algorithm in CBC mode. Through the decryption process, the ciphertext can be restored to its original plaintext form without altering the data, while all independent paths can be executed according to the logic flow designed for the program. Therefore, the implementation of AES-128 in CBC mode supports the confidentiality of customer data in a web-based WiFi provider information system through the applied encryption mechanism.
KLASIFIKASI INTENSITAS HUJAN PER JAM MENGGUNAKAN 1D-CNN BERBASIS TINYML DENGAN KALIBRASI AMBANG PRECISION-RECALL Revangga Kusuma Dhani; Agustina Rachmawardani; Marzuki Sinambela; Adi Widiatmoko Wastumirad
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8794

Abstract

Hydrometeorological disasters dominate annual disaster occurrences in Indonesia, yet local rainfall prediction remains challenging due to atmospheric complexity and limited resolution of numerical weather models. Server-based forecasting systems further depend on high-performance computing infrastructure and stable network connectivity that are not always available in the field. This study develops an hourly rainfall intensity classification model based on One-Dimensional Convolutional Neural Network (1D-CNN) deployable on an ESP32-S3 microcontroller as a proof-of-concept inference component for rainfall early warning systems. The model uses nine meteorological features arranged in an 18×9 sliding window derived from observational data from the BMKG Soekarno-Hatta Meteorological Station AWS from 2018 to 2025. Logarithmic class weighting and Precision-Recall curve threshold calibration were applied to address extreme class imbalance in hourly resolution data. The model was compared against five baseline models under identical configurations. Threshold calibration increased K2 recall from 0.037 to 0.236 and improved Macro-F1 from 0.461 to 0.530, outperforming all baseline models in terms of Macro-F1. Post-training quantization INT8 reduced model size from 185.8 KB to 64.8 KB with 99.02% decision agreement against the Float32 model. On-device inference on ESP32-S3 achieved a total latency of 13.873 ms with 36.5 KB tensor arena and 234.7 KB free heap, confirming real-time operation without dependence on servers or internet connectivity.

Page 48 of 48 | Total Record : 475

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