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Taswanda Taryo
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Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences Other

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PENERAPAN SISTEM KONTROL SUHU PID AUTOTUNING UNTUK OTOMATISASI PROSES EKSTRAKSI KAFEIN KONVENSIONAL DARI BIJI KOPI JAVA ROBUSTA Nikolaus Dharmawan Dharsono; Susanto Pausinugroho; Abdul Multi; Taswanda Taryo
Journal of Innovation Research and Knowledge Vol. 5 No. 9 (2026): Februari 2026
Publisher : Bajang Institute

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

Abstract

Kopi Java Robusta merupakan salah satu komoditas potensial sebagai sumber kafein untuk aplikasi farmasi dan pangan. Namun, proses ekstraksi kafein sangat dipengaruhi oleh kestabilan suhu, sementara banyak sistem ekstraksi yang digunakan masih bersifat konvensional dan kurang akurat. Masalah utama yang dihadapi adalah ketidakmampuan sistem pemanas menjaga suhu pada setpoint sehingga menurunkan efisiensi ekstraksi. Untuk mengatasi hal tersebut, penelitian ini menerapkan sistem kontrol suhu berbasis PID autotuning pada alat ekstraksi kafein dengan variasi rasio kopi: pelarut (1:10 dan 1:15) serta suhu ekstraksi 60°C dan 70°C. Sistem kontrol dievaluasi dari kemampuan mencapai suhu target dan stabilitas selama proses ekstraksi. Hasil menunjukkan bahwa rasio 1:10 mampu mencapai setpoint pada suhu 60°C dan 70°C, meskipun terjadi overshoot sebesar 7°C pada 60°C dan 4°C pada 70°C, sedangkan rasio 1:15 tidak mampu mencapai suhu target karena keterbatasan daya pemanas. Kadar kafein tertinggi diperoleh pada kondisi 70°C dengan rasio 1:10 sebesar 2,4%. Analisis faktorial menunjukkan bahwa rasio kopi: pelarut dan suhu merupakan faktor utama yang mempengaruhi jumlah kafein terekstrak, sementara interaksi antar variabel tidak signifikan. Secara keseluruhan sistem kontrol suhu yang diterapkan menunjukkan kemampuan untuk mendukung proses ekstraksi secara otomatis, proses kontrol suhu masih memerlukan finetuning agar kontrol suhu lebih stabil
ANALYSIS OF WORK CULTURE, COMPETENCIES, CAREER DEVELOPMENT ON EMPLOYEE PERFORMANCE THROUGH COMPENSATION AT PT PELINDO REGIONAL 2 NORTH JAKARTA Roy Qurrotu Ainin; Agustina Mogi; Taswanda Taryo
Multidisciplinary Indonesian Center Journal (MICJO) Vol. 3 No. 1 (2026): Vol. 3 No. 1 Edisi Januari 2026
Publisher : PT. Jurnal Center Indonesia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62567/micjo.v3i1.2210

Abstract

This study aims to analyze the effects of Work Culture, Competence, and Career Development on Employee Performance through Compensation at PT Pelindo Regional 2 North Jakarta. The research employs a quantitative and descriptive approach. The sampling technique used is probability sampling with a simple random sampling method, involving 241 respondents. The analytical tool applied in this study is Partial Least Squares–Structural Equation Modeling (PLS-SEM) version 4.0. The results of the study can be summarized as follows: (1) Work culture has a positive effect on compensation with a path coefficient of 0.195; (2) Competence has a positive effect on compensation with a path coefficient of 0.110; (3) Career development has the strongest positive effect on compensation with a path coefficient of 0.532; (4) Work culture has a negative effect on employee performance with a path coefficient of –0.066; (5) Competence has a positive effect on employee performance with a path coefficient of 0.060; (6) Career development has a negative effect on employee performance with a path coefficient of –0.204; (7) Compensation has a positive and the most dominant effect on employee performance with a path coefficient of 0.764; (8) Work culture has a positive indirect effect on employee performance through compensation with a path coefficient of 0.149; (9) Competence has a positive indirect effect on employee performance through compensation with a path coefficient of 0.084; and (10) Career development has the strongest indirect effect on employee performance through compensation with a path coefficient of 0.406. Based on these findings, it is recommended that the management of PT Pelindo Regional 2 prioritize a fair and performance-based compensation system, as it has been proven to be the most dominant factor in improving employee performance. Furthermore, career development and competence enhancement should be accompanied by proportional rewards and welfare improvements to ensure productive outcomes and encourage optimal employee contributions.
Comparative Analysis of GRU and MLP Models for Extreme Rainfall Nowcasting Using AWS Hartanto; Sajarwo Sanggai; Taswanda Taryo; Ananda, Naufal
Journal of Renewable Energy and Smart Device Vol. 3 No. 2 April 2026
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66314/joresd.v3i2.421

Abstract

Accurate forecasting of rainfall intensity is critical for hydrometeorological disaster mitigation in tropical regions like Indonesia. While high-resolution AWS data provides an opportunity to improve forecasting over conventional manual gauges, processing this volatile time-series data requires advanced computational models. This study comparatively evaluates predictive performance of static feed-forward MLP and sequential memory GRU deep learning architecture. Utilizing a three-year dataset (2022–2024) from three stations representing coastal, lowland, and mountainous topographies, the study reconstructed minute-aggregated AWS data using a sliding window algorithm. This successfully validated the digital sensors against manual Hellmann-type rain gauges, achieving a strong correlation (R > 0.80) for hourly accumulations. Both deep learning models were then trained using historical rainfall, temperature differences, and humidity differences. The empirical results demonstrate that the GRU model quantitatively outperforms the MLP, achieving a higher average classification accuracy of 96.49% (compared to 95.49%) and a lower RMSE of 1.51 mm (compared to 1.59 mm). The GRU’s gating mechanism proved significantly more robust in handling sharp data fluctuations across diverse terrains. However, the analysis revealed a shared structural limitation: both architectures severely underestimated extreme peak rainfall amplitudes. This anomaly stems from the inherent sparsity of extreme weather data and the mathematical conservatism induced by MSE loss function. Ultimately, while the GRU is highly recommended as a reliable frontline trigger for early warning systems, estimating absolute extreme rainfall magnitudes necessitates future exploration of non-standard loss functions and spatial data integration.
Co-Authors Abdul Multi Achmad Hindasyah AGUNG BUDI SUSANTO Agus Sofwan Agustina Mogi Ahmad Musyafa Ajeng Permata Suri Ananda, Naufal Anggai, Sajarwo Arya Adhyaksa Waskita BAHRUL ULUM Baskoro Abhi P Dahlan Supriatna Desmayadi Desmayadi Entis Sutisna Farkhan Mubarok Gregorius Eduard D. P. H. E. Nurzaman Hartanto Iqbal Habib Al Baqi Komariah Kukum Manieh Nuryasin Kun Wardana Masbah R.T. Siregar Muhamad Sibli Mukhlis Catio Mukhlishoh Syaukati Robbi Murni Handayani Nahampun, Rajin Nikolaus Dharmawan Dharsono Nilovar Asyiah Nining Suharwati Nursalam, Asep Herman Permana, Surya R. Suharyadi Reno Saeprani Riani, Ambar Roy Qurrotu Ainin Sajarwo Sanggai Sarwani Sarwani, Sarwani Sewaka Sri Vidawati Sudarno Suharno Suharno Sukanda, Ahmad Suriansjah Suriansjah Susanto Pausinugroho Trijanitra, Evan Uliyatunisa Waskita , Arya Adyhaksa Winarni Yustini Yustini Zhafar Usamah