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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Ilmiah KOMPUTASI Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Sains Dan Teknologi (SAINTEKBU) Advance Sustainable Science, Engineering and Technology (ASSET)
Suharjito
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Physics

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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.
Green Supply Chain Management Innovation in Promoting Sustainability in Indonesia's Palm Oil Industry Putro, Danar Adityo; Suharjito
Jurnal Penelitian Pendidikan IPA Vol 11 No 5 (2025): May
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i5.10951

Abstract

The palm oil industry is vital to Indonesia’s economy but faces regulatory, environmental, and cost challenges, driving the need for sustainable supply chains. This study examines the impact of Green Supply Chain Management (GSCM) on corporate sustainability performance and the mediating role of Green Innovation. It focuses on Green Procurement, Manufacturing, Logistics, and Packaging. This study a quantitative approach, data was collected from industry specialists through structured questionnaires. The research follows a structured process: problem identification, literature review, hypothesis development, data collection, and analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM). PLS-SEM was chosen for its suitability in handling complex models, formative constructs, and non-normal data, making it ideal for predicting GSCM’s impact on CSP Data from 100 managers across more than 20 companies were analyzed using PLS-SEM with WarpPLS 6.0, which handles non-linear and complex relationships. Findings show GSCM significantly improves economic, environmental, and social sustainability, with Green Innovation mediating economic and social impacts but not environmental ones. The study underscores the need to align GSCM with SDGs for long-term sustainability and offers strategic insights for academics and practitioners.
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.
Co-Authors Ahmad Yusuf Al-Majid August, Ryan Alpha Budi Prasetyo Pieter, Franky Bryan Putro, Danar Adityo Ridwan Petervan Siburian, Firman