Jurnal Politeknik Negeri Batam (PoliBatam)
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Psychological Drivers of Consumer Boycott: Understanding Emotional and Social Identity Influences in Batam
Get PDFConsumer boycotts have become an increasingly significant form of consumer activism, driven by ethical, political, and social concerns. This study explores the psychological motivations behind boycott participation in Batam, focusing on emotional triggers, social identity influences, and perceived effectiveness. Using a qualitative approach, data were collected through in-depth interviews and focus group discussions with 30 respondents from Generation X, Y, and Z. Thematic analysis was conducted manually, highlighting key motivations such as moral responsibility, peer influence, and digital activism. The findings reveal that Gen Z engages in boycotts as an expression of online activism, Gen Y is driven by ethical consumption, and Gen X remains skeptical and pragmatic. This research contributes to the literature on consumer activism, ethical consumption, and social identity theory, offering insights for businesses, policymakers, and advocacy groups on how to navigate boycott movements and maintain consumer trust
Maximizing Profit Margins: The Interconnection Between Working Capital Efficiency and Sales Growth
Get PDFThis study explores the influence of working capital efficiency on profitability, emphasizing the moderating role of sales growth. Utilizing panel data from publicly listed companies in food and beverage companies listed on the Indonesia Stock Exchange for the 2018-2021 period, the research employs multiple linear regression analysis to assess the direct impact of working capital efficiency, measured by the Working capital efficiency, on profitability, measured by Return on Assets (ROA). The analysis further incorporates sales growth as a moderating variable to evaluate its interaction with working capital efficiency. The findings confirm a positive relationship between working capital efficiency and profitability, underscoring the importance of optimizing current asset and liability management. Additionally, the results demonstrate that sales growth significantly moderates this relationship, amplifying the positive impact of working capital efficiency on profitability. Firms experiencing robust sales growth benefit more from efficient working capital practices, as higher revenues enhance liquidity and resource utilization. Conversely, firms with stagnant or declining sales face limitations in leveraging the benefits of working capital optimization. This research contributes to the existing literature by highlighting the dynamic interplay between working capital efficiency and sales growth, offering a nuanced perspective on profitability determinants. The findings provide actionable insights for managers, suggesting a dual-focus strategy of enhancing working capital efficiency and fostering sales growth to maximize financial performance. Future studies could expand on this framework by exploring additional moderating variables, sector-specific dynamics, and long-term implications in diverse economic contexts
Analisis Pengukuran Produktivitas Bagian Produksi Dengan Metode Objective Matrix (Omax) Pada Pabrik Roti Dinamis
No full textDynamic Bread Factory has suffered a very significant failure to reach the production target by finding a large number of defective products so that the use of materials becomes inefficient and also often the breakdown of the processing machine which affects the quantity of the final product becomes unoptimal. The study aims to analyze the index of productivity value on the production part of the Dynamic Bread Factory, identify factors causing the fluctuation of the production index and provide suggestions for improvements that can affect the increase in productiveness. OMAX methods and fishbone diagrams to measure and analyze productivity indices. The data processing results showed that the index varied, with the highest index in November increasing by 215.48% while the lowest in October experienced a significant decline of 73.50%. Five productivity ratios that have been measured indicate that ratio 1 and ratio 2 have the lower yields that are the cause of low productivities in the Dynamic Bread Factory.Pabrik Roti Dinamis mengalami ketidak capaian target produksi yang sangat signifikan dengan ditemukannya produk cacat yang cukup banyak sehingga penggunaan bahan menjadi tidak efisien dan juga sering rusaknya mesin pengadon yang mempengaruhi jumlah produk akhir menjadi tidak optimal. Penelitian ini bertujuan untuk menganalisis indeks nilai produktivitas pada bagian produksi di Pabrik Roti Dinamis, mengidentifikasi faktor–faktor penyebab fluktuatif indeks produktivitas dan memberikan usulan perbaikan yang dapat mempengaruhi peningkatan produktivitas. Metode OMAX dan fishbone diagram untuk mengukur dan menganalisis indeks produktivitas. Hasil pengolahan data menunjukkan indeks bervariasi, dengan indeks tertinggi pada bulan November yang mengalami peningkatan sebesar 215,48 % sedangkan bulan Oktober dengan indeks terendah mengalami penurunan yang signifikan yaitu 73,50%. Kelima rasio produktivitas yang telah diukur menunjukkan bahwa rasio 1 dan rasio 2 memiliki hasil terendah yang menjadi penyebab rendahnya produktivitas di Pabrik Roti Dinamis
Identifikasi Penyebab Terjadinya Landing Gear Indication Tidak Berfungsi Normal pada Pesawat Boeing 737-900 ER
No full textThe Boeing 737-900 ER aircraft has a system called landing gear indication which in its application uses a proximity sensor that is useful in providing information about the position of the landing gear. However, this system can experience problems where if the landing gear indication does not function, the pilot cannot obtain information from the instrument that ensures the position of the landing gear. The purpose of this study is to identify problems that often occur in the landing gear indication system on the aircraft. The analysis was carried out using the Root Cause Analysis (RCA) method with the Fishbone Diagram approach. From the identification results, it was found that the main cause was a problem with the proximity switch sensor experiencing corrosion and changes in position which caused the signal reading to be inaccurate. For this reason, it is necessary to replace components and adjust the sensor position so that the system can run properly.Pesawat Boeing 737-900 ER mempunyai sistem yang dinamakan landing gear indication yang dalam aplikasiannya menggunakan sensor jarak yang berguna dalam memberikan informasi tentang posisi roda pendaratan. Namun, sistem ini dapat mengalami permasalahan dimana apabila landing gear indication tidak berfungsi maka pilot tidak dapat memperoleh informasi dari instrumen yang memastikan posisi landing gear. Tujuan penelitian ini yaitu mengidentifikasi permasalahan yang sering terjadi pada landing gear indication system di pesawat. Analisis yang dilakukan menggunakan metode Root Cause Analysis (RCA) dengan pendekatan Fishbone Diagram. Dari hasil identifikasi ditemukan bahwa penyebab utama yaitu permasalahan pada sensor proximity switch mengalami korosi dan perubahan posisi yang menyebabkan pembacaan sinyal menjadi tidak akurat. Untuk itu perlu adanya pergantian komponen dan pengaturan posisi sensor agar sistem dapat berjalan dengan baik
Analisis Konsumsi Energi Listrik pada Mesin Pengering Cabai dengan Variasi Suhu
No full textPenelitian ini bertujuan untuk menganalisis konsumsi dan efisiensi energi Listrik pada mesin pengering cabai dengan variasi suhu dalam durasi waktu 8, 10, 12, 14 dan 16 jam. Hasil pengujian mengindikasikan bahwa adanya peningkatan suhu dan durasi pengeringan sebanding dengan konsumsi energi. Pada suhu 50°C, energi yang digunakan meningkat dari 2,88 kWh dalam durasi 8 jam menjadi 5,77 kWh dalam durasi 16 jam, sedangkan pada suhu 70°C, konsumsi energi mencapai 7,77 kWh dalam durasi 12 jam. Efisiensi energi tertinggi sebesar 8,06% diperoleh pada suhu 50°C selama 16 jam. Laju pengeringan juga meningkat seiring suhu dan waktu dengan nilai tertinggi 1,06 gram/menit pada suhu 70°C selama 12 jam. Namun, kombinasi suhu dan durasi tinggi dapat menyebabkan tekstur cabai menjadi terlalu kering atau keras, serta meningkatkan biaya energi hingga Rp11.655. Oleh karena itu, pemilihan suhu dan waktu pengeringan harus diatur secara tepat untuk mencapai efisiensi, kualitas, dan kecepatan pengeringan yang optimal.Penelitian ini bertujuan untuk menganalisis konsumsi energi listrik pada mesin pengering cabai, serta memperoleh efisiensi energi pada variasi suhu 50°C, 60°C, dan 70°C dalam durasi waktu 8, 10, 12, 14 dan 16 jam. Pada penelitian menunjukkan bahwa peningkatan suhu dan durasi pengeringan berbanding lurus dengan konsumsi energi yang digunakan. Pada suhu 50°C, konsumsi energi meningkat dari 2,88 kWh selama 8 jam menjadi 5,77 kWh selama waktu 16 jam. Sementara itu, pada suhu 70°C, konsumsi energi mencapai 7,77 kWh dalam durasi 12 jam. Dari perspektif efisiensi, suhu 50°C selama 16 jam menghasilkan efisiensi energi optimal sebesar 8,06%. Oleh karena itu, pemilihan suhu dan durasi pengeringan perlu disesuaikan untuk mencapai keseimbangan antara kecepatan pengeringan dan efisiensi energi. Hasil penelitian menunjukkan bahwa semakin tinggi suhu dan lamanya durasi pengeringan maka semakin tinggi laju pengeringan. Suhu 70°C selama 12 jam mendapatkan laju pengeringan 1,06 gram/menit, suhu dan waktu ini juga berpengaruh terhadap tekstur cabai yaitu menjadi terlalu kering atau bahkan menjadi keras selain itu biaya konsumsi energi juga berpengaruh yaitu Rp.11,655, sehingga pemilihan suhu dan waktu pengeringan perlu diperhatikan agar proses pengeringan cabai yang diharapkan dapat optimal
Uji Kinerja Membrane Sea Water Reverse Osmosis Sebelum Dan Sesudah Pemeliharaan Di PLTGU Tanjung Uncang
No full textSea Water Reverse Osmosis (SWRO) di PLTGU Tanjung Uncang berfungsi untuk menyuplai air baku melalui permurnian air laut menggunakan membrane. Dalam operasinya, membrane sering mengalami fouling dan scaling yang menurunkan kinerja sistem. Penelitian ini bertujuan mengevaluasi kinerja membrane SWRO sebelum dan sesudah proses pembersihan menggunakan metode Cleanig in Place (CIP) dan pembersihan manual (pembongkaran). Evaluasi dilakukan berdasarkan data conductivity dan differential pressure dalam tiga kondisi: sebelum Cleanig in Place (CIP), sesudah Cleanig in Place (CIP), dan setelah pembersihan manual. Hasil menunjukkan bahwa Cleanig in Place (CIP) efektif mengurangi fouling ringan, namun tidak cukup untuk fouling berat seperti lumpur. Sebaliknya, pembersihan manual lebih efektif dalam mengembalikan performa membrane ke batas normal (differential pressure ≤ 1,5 bar dan conductivity ≤ 600 µS/cm), meskipun membutuhkan waktu lebih lama. Dengan mengombinasikan kedua metode ini, efisiensi operasional SWRO dapat ditingkatkan secara signifikan.Sea Water Reverse Osmosis (SWRO) di PLTGU Tanjung Uncang berfngsi untuk menyuplai air baku melalui permurnian air laut menggunakan membrane. Dalam operasinya, membrane sering mengalami fouling dan scaling yang menurunkan kinerja sistem. Penelitian ini bertujuan mengevaluasi kinerja membrane SWRO sebelum dan sesudah proses pembersihan menggunakan metode Cleanig in Place (CIP) dan pembersihan manual (pembongkaran). Evaluasi dilakukan berdasarkan data conductivity dan differential pressure dalam tiga kondisi: sebelum Cleanig in Place (CIP), sesudah Cleanig in Place (CIP), dan setelah pembersihan manual. Hasil menunjukkan bahwa Cleanig in Place (CIP) efektif mengurangi fouling ringan, namun tidak cukup untuk fouling berat seperti lumpur. Sebaliknya, pembersihan manual lebih efektif dalam mengembalikan performa membrane ke batas normal (differential pressure ≤ 1,5 bar dan conductivity ≤ 600 µS/cm), mesikpun membutuhkan waktu lebih lama. Dengan mengombinasikan kedua metode ini, efesiensi operasional SWRO dapat ditingkatkan secara signifikan
A Comparative Performance of SMOTE, ADASYN and Random Oversampling in Machine Learning Models on Prostate Cancer Dataset
No full textClass imbalance in medical datasets, including prostate cancer, can affect the performance of machine learning models in detecting minority cases. This study compares three oversampling techniques - SMOTE, ADASYN, and Random Oversampling - to address data imbalance in prostate cancer classification. These techniques are applied to Random Forest (RF), Decision Tree (DT), and LightGBM (LGBM), which are evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. In improving the reliability of the evaluation, K-Fold Cross Validation was used to reduce the risk of overfitting and ensure stable results. The findings show that oversampling techniques improve model performance compared to the baseline. Random Oversampling has the best performance for Random Forest with accuracy 0.85, recall 0.888, precision 0.873, F1-score 0.879, and ROC-AUC 0.838. SMOTE produced the highest Decision Tree performance with accuracy 0.80, recall 0.838, precision 0.843, F1-score 0.839, and ROC-AUC 0.788. ADASYN provided the most improvement for LightGBM, achieving accuracy 0.89, recall 0.919, precision 0.913, F1-score 0.913, and ROC-AUC 0.879. These results confirm that the oversampling method improves prostate cancer classification performance by tailoring the resampling technique to the model characteristics
Implementation of CNN Algorithm for Indonesian Hoax News Detection on Online News Portals
No full textThe Spread of hoax news in the Industrial Revolution 4.0 era has occurred in the world’s society, including Indonesia. Therefore, an effective method is needed to detect it. The purpose of this research is to apply deep learning with the Convolutional Neural Network (CNN) algorithm in detecting text-based hoax news in Indonesian. The dataset is taken from Kaggle, which has been scraped from CNN Indonesia, Tempo, and Turnbackhoax, which will be labeled as valid and hoax. The implementation of the dataset goes through several processes that include input dataset, data pre-processing using pre-trained embedding GloVe, data processing, model evaluation, also model deployment into the simple web. Data is divided into 80% training data and 20% test data for CNN model development. The results show that the CNN model can achieve high accuracy in detecting hoaxes with training accuracy values reaching 99.65% and validation accuracy reaching 99.88% with a loss of 0.0477 and 0.0435, which means that the model is effective in classifying text-based hoax news to the maximum. The model is evaluated using a confusion matrix, precision, recall, and heatmap as a visualization of results. For further research, it is recommended to increase additional variations for training data so the model can understand patterns well
HANA: An AI Chatbot for Islamic Jurisprudence on Menstruation using SBERT and TF-IDF
No full textThe advancement of Artificial Intelligence (AI), particularly in Natural Language Processing (NLP), has opened new opportunities for religious technological innovation, especially in addressing practical Islamic jurisprudence issues such as menstruation (fiqh haid). This research proposes and implements HANA, an AI chatbot developed for Telegram, utilizing a hybrid approach combining Term Frequency-Inverse Document Frequency (TF-IDF) and Sentence-BERT (SBERT) models. A curated dataset of over 1000 question-answer pairs from classical and contemporary Islamic literature was used, primarily based on the Shafi\u27i school of thought. The chatbot matches user queries through a two-stage retrieval: initial keyword matching via TF-IDF and deeper semantic matching via SBERT embeddings. Evaluations were conducted by comparing TF-IDF, SBERT, and hybrid approaches using cosine similarity, precision, recall, and F1-score metrics, focused on top-1 retrieval accuracy. HANA achieved an average cosine similarity score of 0.6581 and a semantic relevance rating of 87% based on expert validation, while User Acceptance Testing (UAT) involving 15 respondents indicated 86.7% satisfaction. Although the system is deployed as a proof-of-concept on Google Colab without persistent hosting, it demonstrates the viability of lightweight AI chatbots for Shariah consultation services. Future improvements include multi-turn conversation handling and integration with large language models for better context understanding. This research contributes to expanding NLP applications within techno-dakwah initiatives, providing a scalable approach to enhance women\u27s access to Islamic jurisprudence knowledge.The advancement of Artificial Intelligence (AI), particularly in Natural Language Processing (NLP), has opened new opportunities for religious technological innovation, especially in addressing practical Islamic jurisprudence issues such as menstruation (fiqh haid). This research proposes and implements HANA, an AI chatbot developed for Telegram, utilizing a hybrid approach combining Term Frequency-Inverse Document Frequency (TF-IDF) and Sentence-BERT (SBERT) models. A curated dataset of over 1000 question-answer pairs from classical and contemporary Islamic literature was used, primarily based on the Shafi\u27i school of thought. The chatbot matches user queries through a two-stage retrieval: initial keyword matching via TF-IDF and deeper semantic matching via SBERT embeddings. Evaluations were conducted by comparing TF-IDF, SBERT, and hybrid approaches using cosine similarity, precision, recall, and F1-score metrics, focused on top-1 retrieval accuracy. HANA achieved an average cosine similarity score of 0.6581 and a semantic relevance rating of 87% based on expert validation, while User Acceptance Testing (UAT) involving 15 respondents indicated 86.7% satisfaction. Although the system is deployed as a proof-of-concept on Google Colab without persistent hosting, it demonstrates the viability of lightweight AI chatbots for Shariah consultation services. Future improvements include multi-turn conversation handling and integration with large language models for better context understanding. This research contributes to expanding NLP applications within techno-dakwah initiatives, providing a scalable approach to enhance women\u27s access to Islamic jurisprudence knowledge
English
No full textThis study uses historical weather data from the Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) to evaluate the performance of two combination machine learning models, LightGBM and CatBoost, in predicting air humidity. Daily weather data including temperature, humidity, rainfall, daylight duration, and wind characteristics are included in the dataset. Among the preprocessing procedures were label encoding, normalization with MinMaxScaler, and managing missing values. Date fields\u27 temporal information was extracted using feature engineering. Both models were optimized using GridSearchCV with three-fold cross-validation after being trained with an 80/20 split. Using R², MAE, and RMSE, the model\u27s performance has been evaluated. CatBoost outperformed LightGBM, which received an R² score of 0.7981, with a better R² score (0.8191) and smaller prediction errors (MAE = 0.0570, RMSE = 0.0744). While feature importance analysis indicated that temperature and seasonal features were important predictors, residual plots validated the models low bias and good generalization. Both models can help with strategic decision-making in climate-sensitive businesses and salt production, according to the results, and are suitable for humidity forecasting