PRARANCANGAN PABRIK FURFURIL ALKOHOL DARI FURFURAL DAN HIDROGEN DENGAN KAPASITAS 65.000 TON/TAHUN

Pabrik Furfuril alkohol dengan kapasitas 65.000 ton/tahun direncanakan akan berdiri pada tahun 2030 di kawasan industri Cilegon Provinsi Banten. Bahan baku yang digunakan adalah Furfural yang didapatkan dari PT. Sree Internasional Indonesia dan Hidrogen dari PT. Air Liquide Indonesia. Furfuril alkohol digunakan sebagai bahan baku atau bahan pendukung di industri pengecoran logam, tekstil, dan industri pemipaan. Saat ini di Indonesia belum terdapat pabrik Furfuril alkohol sehingga masih mengimpor dari negara lain. Diharapkan dengan pendirian pabrik ini dapat memenuhi kebutuhan dalam negeri. Proses pembuatan furfuril alkohol dilakukan menggunakan Reaktor Fixed Bed Multitube yang mereaksikan furfural dan hidrogen dengan bantuan katalis copper sodium silicate. Furfural yang berupa cairan dengan suhu 30 oC, diuapkan menggunakan vaporizer dengan suhu 170 oC agar reaksi yang terjadi pada reaktor berlangsung dengan fase yang sama. Hidrogen diumpankan ke reaktor yang sebelumnya sudah diturunkan tekanannya dari 10 atm menjadi 1 atm menggunakan expansion valve (EV-01). Furfural dan hirdogen masuk ke dalam reaktor untuk direaksikan melewati tube yang berisi katalis dan dikondisikan pada suhu masuk 170 oC dan suhu keluar 170 oC dengan tekanan 1 atm. Kemudian dialirkan menuju separator (SP-01) untuk memisahkan 2 fase, yaitu fase gas dan fase cair. Fraksi ringan yang berupa gas akan di Recycle kembali menuju reaktor, sedangkan fraksi cair diumpankan menuju menara destilasi yang beroperasi pada suhu 160 oC dan tekanan 1 atm. Sebagian bottom akan disimpan dalam tangki penyimpanan produk Furfuril alkohol (T-03) pada suhu 30 oC dan tekanan 1 atm pada kondisi cair, sedangkan lainnya berupa air buang disalurkan ke unit pengolahan limbah. Dilihat dari tinjauan sifat-sifat bahan baku, produk, dan kondisi operasi maka pabrik Furfuril Alkohol ini tergolong sebagai pabrik beresiko tinggi. Hasil analisis ekonomi yang diperoleh yaitu keuntungan sebelum pajak sebesar Rp 110.010.077.304,93 per tahun dan keuntungan setelah pajak sebesar Rp 77.007.054.113,45. Percent Return on Investment (ROI) sebelum pajak 44,88% dan setelah pajak 31,42%. Pay Out Time (POT) sebelum pajak 1,89 tahun dan setelah pajak 2,54 tahun. Break Event Point (BEP) sebesar 48%, Shut Down Point (SDP) sebesar 27,4%, dan Discounted Cash Flow Rate of Return (DCFRR) sebesar 17.50%. Dari data analisis kelayakan tersebut disimpulkan bahwa pabrik ini layak untuk didirikan

Pengabdian Barry Nur setyanto Gasal 2024 2025

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Peran Ekstrakulikuler English Club dalam Melatih Kemampuan Membaca dan Berbicara Bahasa Inggris Siswa di Sekolah Dasar MBS Prambanan

Penelitian ini bertujuan untuk menganalisis pelaksanaan program yang meliputi (pelaksanaan, proses kegiatan, aktivitas siswa, dan kesulitan siswa), menganalisis peran English Club terhadap keterampilan kemampuan membaca dan berbicara bahasa Inggris siswa, dan mengidentifikasi faktor-faktor yang mempengaruhi keterampilan membaca dan berbicara bahasa Inggris siswa peserta English Club di SD Muhammadiyah MBS Prambanan. Metode yang digunakan pada penelitian ini yaitu penelitiam kualitatif. Penelitian ini dilaksanakan di SD uhammadiyah MBS Prambanan, yang terletak di daerah Cepit, Bokoharjo, Prambanan, Sleman, DIY. Informan dalam penelitian ini ialah guru pembina ekstrakulikuler English Club dan siswa kelas 4 dan 5 yang mengikuti ekstrakurikuler English Club level 2. Adapun objeknya adalah peran ekstrakurikuler English Club terhadap kemampuan membaca dan berbicara bahasa inggris siswa. Teknik pengumpulan data dalam penelitian ini dilakukan dengan observasi, wawancara, catatan lapangan dan dokumentasi. Keabsahan data menggunakan triangulasi sumber dan triangulasi teknik. Teknik analisis data yang dilakukan adalah pengumpulan data, merangkum, penyajian data dan penarikan kesimpulan. Hasil penelitian menunjukkan bahwa kegiatan ekstrakurikuler English Club telah berhasil membuktikan perannya dalam melatih kemampuan membaca dan berbicara bahasa Inggris siswa. Hal ini didukung oleh pelaksanaan kegiatan, peran guru pembimbing, keaktifan dan motivasi siswa, serta dukungan sekolah. Siswa menunjukkan peningkatan yang signifikan dalam kemampuan membaca dan berbicara melalui berbagai kegiatan seperti membaca, berdiskusi, berdebat, berdialog, dan mendengarkan. Selain itu, siswa juga lebih aktif dan percaya diri dalam menggunakan bahasa Inggris dalam konteks sehari-hari. Meskipun terdapat beberapa kendala, seperti rasa malu pada diri siswa, namun dukungan dari guru dan suasana belajar yang menyenangkan membantu mengatasi kendala tersebut. Kontribusi penelitian ini diharapkan dapat memberikan wawasan tentang efektivitas kegiatan ekstrakurikuler dalam pengajaran bahasa Inggris di jenjang SD

Prediction of Purchase Volume Coffee Shops in Surabaya Using Catboost with Leave-One-Out Cross Validation

Indonesia's coffee consumption grew from 265,000 tons in 2015 to 294,000 tons in 2020. Averaging 2% annual growth with a projected 368,000 tons by 2024. One of the coffee businesses is coffee shops, Coffee shop businesses often struggle to attract customers quickly, risking low purchase volume within their first five years. In their first year, challenges include management, company size, service quality, and customer preferences. This study adopts a quantitative approach and new solutions to develop a purchase prediction application based on machine learning and strategy to enhance purchase volumes for three coffee shops in Surabaya. It utilizes CatBoost, with LightGBM as a comparison, across multiple coffee shop locations. LOOCV (Leave-One-Out Cross-Validation) is used in this model to address research limitations, such as data overfitting and biases, while enhancing evaluation accuracy. As a result, the study established CatBoost as the superior model for purchase prediction, providing insights and practical applications in business forecasting. The Catboost model achieved an MAE of 0.91 and MAPE of 15%, outperforming LightGBM’s MAE of 1.13 and MAPE of 18%. These results confirmed CatBoost’s effectiveness for the coffee shop industry with good accuracy. This research also contributes to helping coffee shop owners in Surabaya understand market characteristics, such as the most profitable coffee types and high-customer-density locations. Additionally, it aids in optimizing purchase volume to leverage profit by developing new strategies based on prediction result. In conclusion, CatBoost accurately predicts purchase volume, helping coffee shops identify target markets and refine strategies based on customer preferences

Enhancing Refactoring Prediction at the Method-Level Using Stacking and Boosting Models

Refactoring software code is crucial for developers since it enhances code maintainability and decreases technical complexity. The existing manual approach to refactoring demonstrates restricted scalability because of its requirement for substantial human intervention and big training information. A method-level refactoring prediction technique based on meta-learning uses classifier stacking and boosting and Lion Optimization Algorithm (LOA) for feature selection. The evaluation of the proposed model used four Java open source projects namely JUnit, McMMO, MapDB, and ANTLR4 showing exceptional predictive results. The technique successfully decreased training data necessities by 30% yet generated better prediction results by 10–15% above typical models to deliver 100% accuracy and F1 scores on DTS3 and DTS4 datasets. The system decreased incorrect refactoring alert counts by 40% which lowered the amount of needed developer examination

Newspaper Ad Submission and Payment Website Measurement Analysis Using McCall and PIECES

The transition to digital platforms in the media industry requires robust systems to ensure efficiency and user satisfaction. As with Digital Iklan Radar Banjarmasin, the Newspaper ad submission and payment website, there is a need for evaluation to comprehensively ensure software feasibility and quality. This research evaluates the quality of the Newspaper ad submission and payment website using the McCall and PIECES frameworks, comparing their strengths and identifying areas for improvement. This research contributes to determining the most suitable evaluation methods for such types of websites while offering actionable insights for developers to improve the quality of systems and services. Data collection involved online surveys with 106 respondents and 38 Likert-scale questions mapped to McCall and PIECES frameworks. Statistical tests, including validity, reliability, and an independent t-test, were applied to compare results. McCall's evaluation rated the system at 68% (Good), with low scores in Usability (38.5%), Reliability (36.77%), and Efficiency (38.15%), indicating areas needing significant improvement. PIECES evaluation scored 80.4% (Good), with Performance (81%) and Service (82.39%) rated Very Good, though Control and Security (78.55%) required enhancement. Statistical analysis with independent t-test confirmed significant differences between the two methods, indicating that both methods measure aspects of software quality from different perspectives, thus providing complementary insights for evaluation. The study highlights the complementary nature of McCall and PIECES in software quality evaluation. Recommendations include improving usability, system stability, and security for better user experiences. Future research should involve broader demographic samples and different system types to validate findings and enhance generalizability

A Machine Learning-Based Approach for Retail Demand Forecasting: The Impact of Spending Score and Algorithm Optimization

Demand forecasting in the retail industry remains a critical challenge, with inaccurate predictions leading to substantial inventory inefficiencies, financial losses, and reduced customer satisfaction. Traditional forecasting methods, primarily reliant on historical sales data, often lack the capacity to effectively model the complexities of dynamic consumer behavior and rapid market fluctuations. To address this, this study proposes a refined demand forecasting approach through the introduction of the Spending Score, a novel synthetic feature that synthesizes customer purchase frequency and total spending to augment predictive accuracy. We implement and optimize machine learning algorithms, specifically Random Forest, Decision Tree, and Support Vector Machine (SVM), using rigorous hyperparameter tuning techniques to determine the most effective model for retail demand prediction. Utilizing detailed customer transaction data, this research aims to identify key purchasing patterns that significantly influence demand variability. By integrating the Spending Score into our predictive models, we provide a data-driven framework enabling retailers to optimize inventory management, enhance targeted marketing strategies, and minimize operational inefficiencies. Empirical results demonstrate that the inclusion of the Spending Score leads to more stable and accurate demand forecasts, facilitating improved alignment between supply and market demand. While acknowledging potential limitations, including data scalability issues and the risk of feature-induced bias, future research will explore the integration of real-time data streams, advanced deep learning methodologies, and expanded datasets to further improve predictive capabilities and model adaptability in the continuously evolving retail landscape