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Ronal Watrianthos
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Journal of Systems Engineering and Information Technology
Published by Organisasi Profesi IKATAN AHLI INFORMATIKA INDONESIA
ISSN : -     EISSN : 2829310X     DOI : https://doi.org/10.29207/joseit.*
Core Subject : Science,
Computer Science & IT
International Journal of Systems Engineering and Information Technology (JOSEIT) is an international journal published by Ikatan Ahli Informatika Indonesia (IAII / Association of Indonesian Informatics Experts). The research article submitted to this online journal will be peer-reviewed. The accepted research articles will be available online (free download) following the journal peer-reviewing process. The language used in this journal is English. JOSEIT is a peer-reviewed, blinded journal dedicated to publishing quality research results in Computers Engineering and Information Technology but is not limited implicitly. All journal articles can be read online for free without a subscription because all journals are open-access.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
 1 
Search results for , issue "vol. 4 no. 3 (2025)" : 5 Documents clear
Does Bigger Beat Simpler? Multilayer Perceptrons versus Recurrent Networks in Trade Export Price Forecasting Kim Ji Hun; Lee Jee Hang
Journal of Systems Engineering and Information Technology (JOSEIT) Vol. 4 No. 3 (2025)
Publisher : Ikatan Ahli Informatika Indonesia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/8szpka26

Abstract

South Korea’s trade agency, KOTRA, publishes annual trade data under the Ministry of Trade, Industry and Energy that reports gross domestic product (GDP), customs tariffs, an ease-of-business score, and the previous- and current-year price for each traded item, broken down by item and by country. The sheer volume of items and years this data accumulates over makes it hard to extract a meaningful signal by hand, and manual analysis every year is costly in both time and expertise. This paper implements and evaluates a multilayer-perceptron (MLP) model that learns from a single year of this large-scale data to forecast next-year item-level export prices at low computational and human cost. The deep-learning model is first compared against a conventional multivariate-regression model, where it shows a statistically better prediction error and shorter training time. Because export-price data plausibly carries time-series structure, the same task is also modeled with a fully connected MLP and with a recurrent neural network (RNN). While the recurrent model generalizes somewhat better to unseen data, the MLP achieves the stronger overall prediction accuracy on this single-year dataset. Given a longer time series in future work, a recurrent or Transformer-based deep-learning model is expected to improve export-price prediction further.
A TF-IDF Symptom-Matching Framework for Tabular Knowledge-Based Tomato Disease Decision Support Akinde Ogunmolu
Journal of Systems Engineering and Information Technology (JOSEIT) Vol. 4 No. 3 (2025)
Publisher : Ikatan Ahli Informatika Indonesia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/bnnejy35

Abstract

Early and accurate field identification of tomato diseases is critical for timely intervention, yet farmers in resource-limited settings frequently lack access to expert diagnosis. This paper proposes a lightweight, knowledge-based decision support system (DSS) for differentiating nine economically important tomato diseases and pests using a recently published tabular knowledge base. The DSS operates in two stages: first, it represents each disease's symptom description as a TF-IDF vector (unigram and bigram, lemmatized, with domain-specific stopword filtering) and retrieves the best-matching disease via cosine similarity to an observed symptom query; second, it refines the ranking using a rule-based environmental condition filter (warm-humid or cool-wet) when field context is available. We evaluate the framework on 121 individual symptom sentences extracted from the knowledge base as independent field-observation queries. The symptom-similarity stage alone achieves a sentence-level top-1 retrieval accuracy of 99.2% (120/121 sentences), with only one Mosaic Virus sentence – the non-specific phrase ‘stunted growth and reduced yields’ – initially misranked. Symptom cosine similarity analysis reveals that Target Spot is the most structurally confusable disease, sharing the highest off-diagonal similarity with Early Blight (0.144), Late Blight (0.137), and Septoria (0.131). Treatment complexity analysis further shows a clinically important distinction between fungal/pest diseases (20 or more chemical treatment options each) and viral diseases (Mosaic Virus: 2; Yellow Leaf Curl Virus: 1), where chemical interventions are ineffective and prevention-only strategies are documented.
Data Integrity and Its Impact on Machine Learning Models: A Critical Audit of a Lung Cancer Dataset Syeda Afroz Fatima; Nida Mussarat; Swarna Jaya Vardhan
Journal of Systems Engineering and Information Technology (JOSEIT) Vol. 4 No. 3 (2025)
Publisher : Ikatan Ahli Informatika Indonesia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/dq775h27

Abstract

Subscription-gated IEEE DataPort datasets are difficult to verify before reuse because independent inspection requires access most researchers do not have; we obtained direct access to one such case. We apply a six-stage, three-layer audit (provenance, structural/duplication plausibility, target validity) to “An Anonymized Feature-Based Dataset for Lung Cancer Prediction Using Explainable AI Models,” a 15-feature, binary-symptom-checklist dataset presented as newly compiled to support explainable-AI research. The delivered file contains 2,457 rows, of which 2,132 (86.8%) are exact duplicates of another row, leaving 325 unique records. The dataset’s own hosting page links a peer-reviewed “Research Article” co-authored by two of this dataset’s three listed authors; that 2024 publication states its dataframe “contains 309 entries” with “no null values” — figures that match neither the delivered file’s row count nor its 325 unique records. The same publication’s introduction and methods describe a multi-source study drawing on major imaging and genomic repositories, none of which appear in the delivered file or in the publication’s own reported results, which instead use the same 16-column schema and encoding as a long-circulating public dataset. Within the 325 unique records, duplication is class-dependent: unique cancer-negative rows are repeated 14.1 times on average (up to 86), versus 4.5 times for cancer-positive rows, shifting the delivered file’s class balance relative to the unique data without documentation. Separately, the linked publication reports identical accuracy, precision, recall, F1, and AUC-ROC for five structurally different classifiers, a pattern inconsistent with independent model evaluation.
Addressing Data Leakage and Reproducibility Crises in Machine-Learning-Based Biomechanics Martina Enyanwu Okoh; Amina Ojochide; Riyan Ikhbal Salam; Novi Efendi; Rozi Meri
Journal of Systems Engineering and Information Technology (JOSEIT) Vol. 4 No. 3 (2025)
Publisher : Ikatan Ahli Informatika Indonesia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/z4sxww09

Abstract

Gait-event features dataset intended to support machine-learning-based locomotion analysis, but delivered files are rarely checked against their own documentation or against standard values from the gait-analysis literature. We apply a six-stage, three-layer audit (provenance, structural/physical plausibility, signal validity) to “Robust Gait Event Detection Using GRF and EMG Signals,” a 208-row, 12-feature dataset describing four stance-phase gait events (Heel Strike, Loading Response, Mid-Stance, Heel Off) from 52 participants. Row count, feature count, and participant count all match the hosting page’s Instructions section. However, direct inspection finds two of the twelve documented features are exact duplicates under different names: the column labeled COV (coefficient of variation) is byte-identical to VAR (variance) rather than the standard STD/mean ratio, and the column labeled mode is byte-identical to minimum across all 208 rows, consistent with a computation error rather than genuine central-tendency estimation. An eight-row contiguous block is duplicated verbatim at a fixed 32-row offset, indicating the true unique sample is 50 participants (200 rows), not the documented 52 (208 rows). The file also provides no field distinguishing GRF-derived from EMG-derived features, despite both being named as contributing sources in the dataset’s title and abstract. Finally, while all twelve features discriminate strongly between the four event classes by one-way ANOVA, the percentage feature is exactly 100.000000 with zero variance for every Heel-Off row, consistent with a windowing artifact of the class definition rather than an independently varying measurement, and its per-class averages are inconsistent with standard stance-phase timing reported in the clinical gait-analysis literature.
Dynamic Action Masking and Priority Experience Replay in Deep Reinforcement Learning News Recommenders Yang Zhijie
Journal of Systems Engineering and Information Technology (JOSEIT) Vol. 4 No. 3 (2025)
Publisher : Ikatan Ahli Informatika Indonesia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/7jqyxp41

Abstract

News consumption differs from stable-preference domains: relevance decays within hours and user interest shifts non-sequentially across sessions, motivating deep reinforcement learning (DRL) approaches that plan for sequential engagement rather than static next-click accuracy. A recent proposal, dynamic action-masking deep reinforcement learning (DAMDRL), combines a three-channel GRU state encoder, a TD-error-weighted priority experience cache, and a per-step action mask within a double deep Q-network to address this setting, reporting NDCG@10 and MRR@10 gains over plain DQN and three supervised neural baselines. Independent audits of the wider DRL-recommendation literature, however, have found that a substantial share of such reported gains are not independently reproducible, and in some cases originate from incidental representation-learning effects rather than genuine policy improvement. This paper re-examines the DAMDRL proposal against that critical literature. We restructure the described method into an explicit pipeline and identify eleven specific items — dataset identity, hyperparameters, and training protocol among them — required for replication but absent from the original report. We recompute every comparative and ablation result as explicit percentage deltas, finding NDCG@10 gains of 243–333% over an SVD++ floor and 18–26% losses when priority caching or loop-avoidance exploration is ablated. These patterns are consistent with, but do not rule out, an upstream representation-learning explanation raised by recent reassessment studies of comparable frameworks. We conclude that DAMDRL's central claim is numerically consistent with its own tables but not yet independently verifiable, and provide a reporting checklist to close that gap.

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