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

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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 Budi Prasetyo; 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.
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.
Co-Authors Ahmad Yusuf Al-Majid August, Ryan Alpha Budi Prasetyo Pieter, Franky Bryan Warno