Jakarta State Polytechnic

The printing industry plays a vital role in communication, education, and the creative economy, yet it is also associated with considerable environmental impacts due to high energy consumption, reliance on petroleum-based raw materials, intensive water use, and significant waste generation. In response to increasing societal pressure, regulatory demands, and sustainability-oriented market expectations, Green Manufacturing has emerged as a critical paradigm to reduce the ecological footprint of the printing sector while maintaining its economic and social functions. This study aims to systematically review the existing body of knowledge on Green Manufacturing in the printing industry by identifying dominant research trends, examining key implementation challenges, and proposing a future-oriented research and policy roadmap. A Systematic Literature Review (SLR) was conducted following PRISMA guidelines, synthesizing 35 peer-reviewed international journal articles published between 2014 and 2024 and indexed in major scientific databases. The findings reveal three dominant research streams: (1) innovation in sustainable raw materials, including plant-based inks and recycled or bio-based substrates; (2) process and energy efficiency improvements through digital printing technologies and waterless printing systems; and (3) the adoption of circular economy principles for waste reduction and resource recovery. Despite technological progress, implementation remains constrained by high initial investment costs, technical performance gaps of green materials, fragmented sustainability standards, and limited awareness among consumers and small and medium-sized enterprises (SMEs). Based on identified gaps, this paper proposes a future roadmap emphasizing locally sourced eco-materials, integration of AI and IoT for process optimization, development of globally harmonized sustainability standards, and circular economy–driven business models. By positioning Green Manufacturing as a socio-technical transformation rather than a purely technological shift, this study contributes strategic insights for researchers, industry practitioners, and policymakers seeking to accelerate a sustainable transition in the printing industry.

Incorporating environmental protections in bilateral and regional trade agreements is crucial for fostering sustainable economic development. Early trade agreements focused on economic liberalization, whereas contemporary frameworks increasingly integrate environmental obligations to tackle climate change, resource depletion, and ecological degradation. This paper examines the development of environmental provisions in trade agreements, enforcement mechanisms, and optimal practices for sustainability-oriented trade policy. Prominent case studies such as the CPTPP, EU-Mercosur, and ACFTA, underscore both achievements and obstacles in the execution of environmental requirements. Moreover, market incentives like carbon pricing, border tax adjustments, and green subsidies are essential for harmonizing economic and environmental goals. The research offers policy suggestions for enhancing environmental protections, assuring adherence, and promoting international collaboration. The incorporation of comprehensive sustainability measures in trade agreements will be essential for attaining enduring environmental and economic resilience.

This study evaluated the effect of Eucheuma spinosum carrageenan flour on the chemical quality and sensory acceptance of milkfish nuggets. A completely randomized design with four carrageenan levels (0%, 5%, 10%, and 15%) and two replications was applied from January to April 2024 at Politeknik Pertanian Negeri Pangkajene Kepulauan. Moisture, protein, and fiber contents were analyzed, followed by sensory tests of aroma, taste, and texture. Increasing carrageenan reduced moisture content and increased fiber content. The 15% treatment produced the highest fiber level, whereas the 5% treatment showed the best sensory acceptance among the carrageenan formulations. Therefore, 5% carrageenan is recommended as the most balanced formulation in terms of product quality and consumer preference

Permasalahan yang dikaji berfokus pada bagaimana faktor-faktor eksternal seperti gaji, insentif, penghargaan, dan lingkungan kerja memengaruhi tingkat kesetiaan karyawan terhadap perusahaan. Penelitian ini bertujuan untuk menganalisis hubungan motivasi ekstrinsik dengan loyalitas karyawan pada Perusahaan Roko King Garet Banjarmasin. Penelitian ini menggunakan pendekatan kualitatif dengan metode deskriptif. Data diperoleh melalui wawancara mendalam, observasi, dan dokumentasi terhadap karyawan dan manajemen perusahaan. Informan penelitian dipilih secara purposive berdasarkan masa kerja dan posisi jabatan. Hasil penelitian menunjukkan bahwa motivasi ekstrinsik memiliki peran penting dalam membentuk loyalitas karyawan. Sistem kompensasi yang kompetitif, pemberian bonus berbasis kinerja, serta hubungan kerja yang harmonis menjadi faktor dominan dalam meningkatkan komitmen dan keinginan karyawan untuk tetap bertahan di perusahaan. Namun demikian, ketidakjelasan jenjang karier dan keterbatasan fasilitas kerja menjadi faktor yang berpotensi menurunkan loyalitas. Penelitian ini menyimpulkan bahwa pengelolaan motivasi ekstrinsik yang tepat dan konsisten dapat memperkuat loyalitas karyawan serta mendukung stabilitas organisasi secara berkelanjutan.

Background: In small-scale alcohol distillation, the efficiency of the condenser is critical for product quality and yield. A common problem is the rise in condenser temperature during operation, which diminishes vapor condensation efficiency and results in low alcohol concentration in the distillate. Objective: This research aims to optimize the performance of a shell-and-tube condenser in a 5-liter distillation apparatus by integrating a water jacket system equipped with a thermoelectric (Peltier) cooler. Methods: An experimental approach was employed, comparing the apparatus's performance before and after modification. Key parameters measured included inlet vapor temperature, inlet and outlet cooling water temperatures of the condenser, process duration, and the alcohol concentration of sequential distillate fractions. Results: The findings demonstrate that the thermoelectric water jacket significantly reduced and stabilized the condenser's outlet water temperature throughout the operation. The time to obtain the first distillate decreased from 23 to 16 minutes. The alcohol concentration of the initial fraction increased from 40% to 50%, and the total processing time for the same 5-liter batch was reduced from 55 to 35 minutes. Conclusion: The integration of a thermoelectric water jacket substantially enhances heat transfer and alcohol vapor condensation efficiency. This modification proves to be an effective strategy for improving the thermal performance and product quality of small-scale distillation equipment.

The increase in waste production, along with population growth, is filling landfills. Organic waste leachate formation. Leachate management is crucial because it can pollute soil, surface water, and groundwater. Waste is the solid remains of human activities and natural processes. Waste will be processed at the final processing site. Waste produces leachate due to decomposition and rainwater infiltration. Leachate testing requires laboratory analysis. Analysis can be done by measuring the physical and chemical parameters contained in leachate. Response Surface Methodology (RSM) is a statistical method for analysing data. This study determines the correlation between the main pollutant parameters, Temperature, pH, total suspended solids (TSS), and COD at the final treatment site. Data were collected from several sampling points over a period, and statistical analyses, including Pearson correlation and regression, were used to assess the relationship. The results showed a significant positive correlation with COD. The COD coefficient of determination (R-squared) of 66.74% was found to be quite high for all functional relationships, and mathematical models have been obtained. Contribution to Sustainable Development Goals (SDGs):SDG 6: Clean Water and SanitationSDG 11: Sustainable Cities and Communities

Mining activities in South Kalimantan have had a significant impact on the environment, particularly related to the risk of hydrometeorological disasters such as floods and landslides. Post-mining land reclamation is a crucial effort in mitigating these risks. Sustainable reclamation is key to risk mitigation and can be monitored through the Normalized Difference Vegetation Index (NDVI) as an indicator of vegetation density and health. High NDVI values indicate successful reclamation. The primary objective of this research is to evaluate the effectiveness of post-mining reclamation as a hydrometeorological disaster mitigation strategy by assessing vegetation density and health. The study utilizes Sentinel-2 Level-2A satellite imagery processed on the Google Earth Engine (GEE) platform to calculate the NDVI. To ensure data reliability, the results were validated through field measurements using the purposive sampling method and canopy cover estimation to achieve a high level of accuracy. The results show an accuracy rate of 90%, with dense vegetation dominating and an index value of 0.6–0.9, indicating that reclamation has successfully increased vegetation cover and plays a significant role in mitigating floods and landslides.

Background: The Class II Non-TPI Muara Enim Immigration Office processes thousands of passport applications annually. However, existing data utilization remains limited to administrative recording and routine reporting, constraining its strategic value for service planning. Objective: This study aims to identify meaningful patterns in passport application data and determine the optimal cluster structure using the K-Means algorithm to support evidence-based decision-making. Methods: Therefore, this study was conducted to apply data mining techniques using the K-Means clustering method to analyze passport application patterns based on data from 2022 to 2024, with attributes such as gender, travel purpose, type of application, type of passport, and year of submission, implemented through Python with the support of Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn libraries. Results: The results show that passport applicants can be grouped into several clusters with specific characteristics — for instance, clusters dominated by applicants of productive age with purposes of working or performing Umrah, as well as other clusters consisting mainly of applicants traveling for tourism or education; moreover, the analysis reveals spikes in applications during certain periods, such as before the Hajj season and long holidays. Conclusion: Thus, it can be concluded that applying K-Means clustering provides added value beyond conventional data processing, enabling administrative data to be transformed into informative and predictive patterns that support resource planning, staff allocation, and more effective data-driven service policies.

Perkerasan jalan merupakan infrastruktur penting yang berfungsi mendukung mobilitas dan aktivitas transportasi. Salah satu jenis perkerasan lentur yang banyak digunakan adalah aspal porus, karena memiliki rongga udara tinggi yang mampu menyerap air dan mengurangi genangan di permukaan jalan. Penelitian ini bertujuan untuk mengetahui pengaruh substitusi ban dalam bekas sepeda motor terhadap karakteristik Marshall pada campuran aspal porus serta menentukan kadar substitusi yang paling optimum sesuai spesifikasi AAPA (2004). Pemilihan campuran aspal porus didasari oleh sifatnya yang memiliki rongga udara tinggi (void) sehingga mampu menyerap air dan mengurangi genangan di permukaan jalan, namun kelemahannya terletak pada stabilitas yang relatif rendah terhadap beban lalu lintas berat. Oleh karena itu, dilakukan inovasi dengan menambahkan ban dalam bekas sebagai aspal panas untuk meningkatkan stabilitas sekaligus memanfaatkan limbah karet yang sulit terurai di lingkungan. Penelitian ini dilakukan pada tahun 2025 di Laboratorium Perkerasan Jalan Universitas Almuslim menggunakan metode uji Marshall dengan variasi kadar substitusi ban dalam bekas sebesar 0%, 1%, 2%, 3%, dan 4%. Hasil penelitian menunjukkan adanya tren peningkatan nilai stabilitas seiring bertambahnya kadar substitusi, dengan nilai tertinggi sebesar 1178 kg pada kadar 4%. Nilai flow meningkat dari 2,2 mm menjadi 3,0 mm, VIM sedikit menurun dari 19% menjadi 16%, dan MQ meningkat dari 383 kg/mm menjadi 398 kg/mm. Seluruh parameter tersebut masih memenuhi spesifikasi AAPA (2004). Berdasarkan hasil tersebut, kadar substitusi 4% adalah nilai paling optimum karena menghasilkan keseimbangan terbaik antara stabilitas, kelelehan (flow), dan rongga udara (VIM). Penelitian ini menunjukkan bahwa pemanfaatan limbah ban dalam bekas berpotensi meningkatkan performa aspal porus sekaligus mendukung penerapan teknologi perkerasan jalan yang ramah lingkungan.

Mental health disorders constitute a major global public health concern, affecting millions of individuals across diverse socioeconomic and cultural contexts. Accurate prediction of mental health outcomes at the population level remains challenging due to the complex and non-linear relationships among co-occurring disorders. Previous studies relying on traditional statistical approaches, particularly linear regression, have reported limited predictive performance, with an R² of approximately 0.7175. This limitation highlights the need for more advanced analytical frameworks capable of capturing comorbidity patterns and non-linear interactions among mental health conditions. This study proposes and evaluates a novel multi-modal ensemble machine learning framework to improve the prediction accuracy of eating disorder prevalence using global mental health data. The analysis utilizes country-level prevalence data for schizophrenia, depression, anxiety, bipolar disorder, and eating disorders across multiple countries and years. Eating disorder prevalence is modeled as the primary target variable, while other mental health disorders are incorporated as predictive features to represent clinically established comorbidity relationships. To enhance the representational capacity of the data, an extensive feature engineering strategy was applied, generating 19 additional features through polynomial transformations, interaction terms, ratio-based indicators, and aggregate burden measures. Unsupervised clustering techniques, including K-Means, DBSCAN, and hierarchical clustering, were employed to identify natural groupings of countries based on their mental health profiles. Furthermore, ten machine learning algorithms were systematically evaluated, including linear models, tree-based methods, neural networks, and support vector regression. The best-performing models were subsequently integrated into a stacking ensemble architecture. Experimental results demonstrate that the proposed stacking ensemble achieved a test R² score of 0.9955, corresponding to a 42.2% improvement over the baseline linear regression model. These results indicate that multi-modal ensemble approaches substantially enhance predictive accuracy and provide valuable insights to support evidence-based global mental health policy and targeted intervention planning.