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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Ilmiah KOMPUTASI Jurnal Sains Dan Teknologi (SAINTEKBU) Advance Sustainable Science, Engineering and Technology (ASSET) Scientific Contributions Oil and Gas
Suharjito
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Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Library & Information Science

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 1 
Technology Development for Detecting Palm Oil Ripeness : A Systematic Literature Review August, Ryan Alpha; Suharjito
Jurnal Ilmiah KOMPUTASI Vol. 20 No. 4 (2021): Jurnal Ilmiah Komputasi Volume: 20 No. 4, Desember 2021
Publisher : Lembaga Penelitian STMIK Jakarta STI&K

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Abstract

Teknologi pendeteksi kematangan kelapa sawit telah berkembang pesat. Salah satu tantangan saat ini adalah sulitnya menentukan kematangan secara akurat dengan menggunakan metode manual. Sebuah tinjauan literatur sistematis dilakukan. Artikel ilmiah diperoleh dari jurnal dan dianalisis untuk mengidentifikasi metode yang sering digunakan oleh peneliti. Berdasarkan kriteria eksklusi, 56 makalah dimasukkan dalam analisis. Klasifikasi dilakukan menurut visi komputer dan sensor. Hasil kajian pustaka menunjukkan bahwa metode yang banyak digunakan oleh peneliti adalah model Jaringan Syaraf Tiruan (JST). Sedangkan Near Infra-Red (NIR) merupakan sensor yang banyak digunakan oleh para peneliti karena sensor ini dapat mengukur kematangan buah dengan biaya yang terjangkau. Berdasarkan tinjauan, dapat disimpulkan bahwa visi komputer dan sensor berkontribusi pada pengukuran kematangan yang akurat dan efisien.
A Comparative Analysis of MultinomialNB, SVM, and BERT on Garuda Indonesia Twitter Sentiment Prasetyo, Budi; Ahmad Yusuf Al-Majid; Suharjito
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 2 (2024): September 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v12i2.9966

Abstract

This study investigates customer sentiment towards Garuda Indonesia Airlines (GIA) using sentiment analysis of Twitter data. The research aims to identify prevailing sentiments, uncover common themes in customer feedback, and provide recommendations for improving customer satisfaction and brand loyalty. A dataset of 1,250 tweets from March 2007 to July 2024 was collected and pre-processed, including cleaning, language detection, and tokenization. Sentiment analysis was conducted using three models: MultinomialNB, SVM, and BERT.The results indicate that BERT outperformed both MultinomialNB and SVM in sentiment classification accuracy, achieving 75.6%. This highlights the effectiveness of BERT in capturing contextual meaning within customer reviews. The findings of this research will contribute to a deeper understanding of customer sentiment towards GIA and inform strategies for enhancing customer experience and brand image.
Forecasting the Palm Oil Market: A Comparative Study of LSTM and Bi-LSTM Models for Price Prediction Pieter, Franky Bryan; Suharjito
SAINTEKBU Vol. 16 No. 02 (2024): Vol. 16 No. 02 August 2024
Publisher : KH. A. Wahab Hasbullah University

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Abstract

This study underscores the critical need for accurate palm oil price predictions amid market volatility, driven by factors like demand shifts and supply disruptions. Employing advanced neural network models, specifically Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM), the research spans May 2007 to December 2022 using Market Insider data. Evaluation metrics, including RMSE 0.000083 and MAPE 0.76%, highlight Bi-LSTM's superior predictive prowess. Beyond immediate benefits for decision-making, the study emphasizes broader impacts on market stability, reducing volatility and fostering sustainability in the palm oil industry. Overall, this paper showcases the efficacy of Bi-LSTM in enhancing palm oil price prediction accuracy, offering practical insights, and contributing to industry sustainability.
Boosting-Based Machine Learning Models and Hyperparameter Tuning for Predicting Vehicle Carbon Dioxide Emission Ridwan Petervan Siburian, Firman; Suharjito
Advance Sustainable Science Engineering and Technology Vol. 7 No. 4 (2025): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i4.2097

Abstract

Sustainable development and climate change are central agendas in global policy and research. This study examines and compares three ensemble learning models using Gradient Boosting Machine, Categorical Boosting, and Extreme Gradient Boosting for forecasting vehicle carbon dioxide (CO2) emissions. Data preprocessing with Interquartile Range (IQR) and median imputation is among the methods used to address missing values in CO₂ rating and smog rating variables. SHAP and PDP were employed for feature importance analysis and model interpretability. The findings from the third experiment demonstrate that Extreme Gradient Boosting (XGBoost) outperformed other models achieving a Coefficient Determination of 0.9988, Root-Mean-Square Error of 2.1696, Mean-Absolute Error of 0.4977, and Mean-Absolute-Percentage Error of 0.0019. The primary predictive features included combined fuel consumption (liters/100 km), city and highway fuel consumption, ethanol fuel consumption, model year, engine size and diesel consumption. The findings suggest the potential of boosting-based models for supporting sustainable transport planning, policy for emission reduction, and evidence-based policy making.
Forecasting the Palm Oil Market: A Comparative Study of LSTM and Bi-LSTM Models for Price Prediction Pieter, Franky Bryan; Suharjito
SAINTEKBU Vol. 16 No. 02 (2024): Vol. 16 No. 02 August 2024
Publisher : KH. A. Wahab Hasbullah University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study underscores the critical need for accurate palm oil price predictions amid market volatility, driven by factors like demand shifts and supply disruptions. Employing advanced neural network models, specifically Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM), the research spans May 2007 to December 2022 using Market Insider data. Evaluation metrics, including RMSE 0.000083 and MAPE 0.76%, highlight Bi-LSTM's superior predictive prowess. Beyond immediate benefits for decision-making, the study emphasizes broader impacts on market stability, reducing volatility and fostering sustainability in the palm oil industry. Overall, this paper showcases the efficacy of Bi-LSTM in enhancing palm oil price prediction accuracy, offering practical insights, and contributing to industry sustainability.
Coiled Tubing Circular Efficiency: A Systematic Literature Review on Failure Mechanisms, Inspection Methods, and Reuse Potential Warno; Suharjito
Scientific Contributions Oil and Gas Vol 48 No 4 (2025)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.v48i4.1906

Abstract

Coiled tubing (CT) has become a critical technology in oil and gas operations, yet its service life is constrained by fatigue, corrosion, and erosion. In marginal fields, the high capital cost of new CT strings for permanent installations such as gas lift creates significant economic challenges. Reusing existing CT assets presents a cost-efficient and sustainable alternative. This study conducts a systematic literature review of 33 Scopus-indexed journal and conference publications to examine CT failure mechanisms, integrity inspection methods, and the economic potential of reuse in marginal fields. The reviewed data were classified by failure mode, inspection technique, application, and economic perspective. The findings reveal that low-cycle fatigue is the most extensively studied failure mode, with wall thickness reduction identified as a key indicator of structural degradation. Current integrity assessments rely heavily on predictive modelling and non-destructive evaluation (NDE) methods, particularly magnetic flux leakage (MFL) and eddy current testing (ECT). Nevertheless, the absence of reliable, field-practical wall thickness measurement remains a critical gap, for which ultrasonic testing (UT) emerges as a promising solution. Case studies further demonstrate the technical feasibility and cost-effectiveness of CT reuse. This review underscores the importance of transitioning from a linear “use-and-scrap” paradigm toward a circular “use-inspect-reuse” framework, with UT serving as a pivotal enabler. This approach enhances economic viability and advances alignment with the United Nations Sustainable Development Goals.
Lean Manufacturing through VSM and SMED for Waste Reduction in Paper-Based Packaging Yumeling; Suharjito
Advance Sustainable Science Engineering and Technology Vol. 8 No. 2 (2026): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i2.2099

Abstract

This study analyzes the reasons for waste in the production processes of a paper-based food packaging company in Indonesia and proposes improvement measures to make the operations more efficient. Through the application of Value Stream Mapping (VSM) and Single-Minute Exchange of Dies (SMED), three significant waste categories were charted: product defects (24.74%), unnecessary movements (22.23%), and unnecessary inventory (13.29%). Implementation of SMED successfully reduced the time of tool change from 60.24 minutes to 27.12 minutes, a setup time improvement of 54.98%. Pursuit of labeling system improvement as well as putting in place a Just-In-Time (JIT) inventory practice also played an important role in the reduction of wastes and fast responsiveness. Consequently, Process Cycle Efficiency (PCE) increased from 70.37% to 73.41%, production capacity increased by 1,241 units per cycle, and complaint counts for defective products declined appreciably over a period of six months. These results underscore the actual benefits of Lean methods and underscore the standard for performance excellence for the same pursuit of sustainable operational excellence within the same industry.
Co-Authors Ahmad Yusuf Al-Majid August, Ryan Alpha Budi Prasetyo Pieter, Franky Bryan Ridwan Petervan Siburian, Firman Warno Yumeling