Sampoerna University

Microalgae are a promising third-generation biofuel feedstock due to their high lipid and carbohydrate content. In this study, Chlorella pyrenoidosa biomass was subjected to alkaline hydrolysis to release fermentable substrates, and the process was optimized using a Box–Behnken response surface methodology. The key parameters – microalgal concentration, NaOH concentration, temperature, and hydrolysis time – were varied to maximize reducing sugar yield. The experimental data were fitted to a statistical model (R²>0.99), which identified significant positive effects of higher biomass loading and longer hydrolysis time on sugar release. Under the optimal conditions, the model predicts a maximum sugar concentration (approximately 0.47–0.50 g/L) from the hydrolysate. These results demonstrate the feasibility of converting Chlorella biomass into biofuel precursors. The findings are discussed in relation to biodiesel production strategies: for example, ultrasound-assisted extraction methods have achieved ~18.8% lipid yield from Chlorella under optimized conditions. Future work should integrate ultrasound pretreatment and lipid recovery (e.g. direct transesterification) to fully exploit microalgal biofuel potential.

This study examines motivations for participating in the understudied Indonesian riots of May 1998 targeting the ethnic Chinese minority, using an integrative framework addressing intergroup, intragroup and individual factors. Semi-structured interviews were conducted with 31 participants (26 male, 5 female), all aged over 35 and involved in violent acts during the riots, in which thousands of homes and businesses were looted or destroyed, and hundreds of people were raped or murdered. Thematic analysis revealed motivations at three levels: intergroup (ethnic prejudice, animosity towards security forces), intragroup (conformity, fear of missing out) and individual (thrill-seeking, need for significance, greed, impulsivity). Narratives illustrate how these factors interact within a context of socio-political and economic upheaval. Most participants cited motivations at the intragroup and individual levels, with fewer referencing intergroup factors or reporting a single level of motivation. Conformity (an intragroup factor) was reported by all participants. This research highlights the complex interplay of psychological and social dynamics driving collective ethnic violence.

Asthma patients are prone to sudden respiratory failure, thus requiring a non-invasive and continuous monitoring method. This study investigates the use of Frequency-Modulated Continuous Wave (FMCW) radar to detect asthma by recording chest displacement signals without physical contact. The extracted respiratory features, including statistical descriptors and Mel-Frequency Cepstral Coefficients (MFCC), are analyzed using a machine learning classifier. To address class imbalance in the dataset, imbalanced learning strategies such as Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) are applied. Among the tested models, the LightGBM-ADASYN combination achieved the best performance with an F1 score of 92.95% on the training data and 92.94% on the testing data. These results demonstrate the effectiveness of integrating FMCW radar with imbalanced learning approaches for the early detection of asthma, enabling timely intervention and improving patient safety.

Abstract This study analyzed the grain size distribution and physical characteristics of the Grogol River bed sediment, Grogol Petamburan District, West Jakarta. This river plays an important role in the urban drainage system, but sediment accumulation causes siltation and a decrease in its capacity, thus increasing the risk of flooding during the rainy season. This study aims to provide technical data for sedimentation control. The method used is quantitative with laboratory tests, including grain size analysis (sieve analysis), specific gravity test, and water absorption. Sediment samples were taken from the river bed at certain points, then dried, sieved, and weighed. The analysis refers to SNI 03-1968-1990 and ASTM C-33. The results showed that 45% of the sediment material passed the No. 200 sieve (0.075 mm), classified as Clayey Sand (SC) according to the Unified Soil Classification System (USCS). The grain size distribution is classified as poorly graded, indicating an uneven and homogeneous size distribution, thus accelerating sedimentation. The fine fraction exceeds the ASTM limit which allows a maximum of 3% to pass the No. 200 sieve for fine aggregate. The bulk specific gravity value of 2.353 gr/cc, bulk SSD specific gravity of 2.415 gr/cc, apparent specific gravity of 2.510 gr/cc, and absorption of 2.7% indicate moderate porosity. These characteristics affect sediment stability and movement, especially when the flow rate changes. Overall, the Grogol River sediment has the potential to accelerate sedimentation, worsen siltation, and reduce the river's hydraulic capacity. These findings form the basis for planning mitigation strategies such as normalization, routine dredging, settling ponds, and adaptive drainage management according to local sediment characteristic

The study explores the model minority myth represented in one of ONE OK ROCK’s songs “Stand Out Fit In” written in English. The concept of model minority attributes the image of Asian American as hard workers in terms of academic and economic achievement in a White society, but it neglects the psychological well-being due to the pressure from Asian American parental expectation and the racism faced by Asian American adolescents. Therefore, the study aims to reconstruct the model minority concept through the analysis of song lyrics and music video scenes of “Stand Out Fit In” by using a discourse analysis framework focusing on the narrative techniques. The result shows that the activity scheme including the repetition of ‘stand out fit in’ in the lyrics and the Bian Lian dance performance in several scenes suggest the new model of model minority proposing the biculturalism concept which is not only maintaining the image of Asian American as high achievers, but also ‘fit[ting] in’ the mainstream society to challenge racism and bias perception of Asian American. This new concept of model minority indicates the cultural adoption strategy, which may lead to Asian Americans' position as ‘honorary white’ in the White supremacy.

Material Requirements Planning (MRP) is a technique in manufacturing and logistics management to effectively schedule and oversee the resources necessary for production procedures. By executing MRP, this research seeks to decrease the expenses related to storing raw materials for a company that manufactures a variety of food items. The organization has successfully met both domestic and global requirements for its goods. Hence, careful planning for each manufacturing phase is essential for improving corporate operations, as it ensures a smooth and continuous workflow. This paper contributes to comparing the Wagner-Whitin algorithm and the Silver-Meal heuristic for lot sizing. The result shows that the Wagner-Whitin algorithm results in the most economical outcome for scheduling each raw material. The total inventory expenses for all raw materials amount to IDR 79,920,930.25.

Janakara Mengabdi community service initiative conducted from February to May 2024 at SDN 07 and SDN 08 Pancoran Pagi, aimed at introducing age-appropriate sexual education to elementary school students through the development and distribution of the “Jaka & Nara Tumbuh Dewasa” storybook. The program included baseline surveys, interactive storytelling sessions, and both physical and digital book distribution. Results indicated a high completion rate of planned activities and positive engagement from students. The baseline data reinforced the need for early education on personal boundaries and body awareness, aligning with current research that shows significant increases in children's knowledge following structured educational interventions. Although the program demonstrated initial success, further steps such as ISBN registration and a sustainability plan for the storybook’s future use remain areas for development. The findings emphasize the role of creative educational tools in empowering children, preventing abuse, and fostering community awareness around child safety and health development.

Rumah merupakan salah satu kebutuhan dasar manusia, di mana masih banyak masyarakat berpenghasilan rendah yang belum memiliki rumah dikarenakan faktor ekonomi. Dengan demikian diperlukan solusi untuk menghasilkan rumah dengan kualitas bagus namun dengan harga terjangkau. Maka perlu dipertimbangkan metode pelaksanaan selain metode konvensional yaitu metode pracetak seperti inovasi dari Kementerian PUPR yaitu teknologi RISHA dan RUSPIN. Cluster Villa Puncak Landungsari merupakan perumahan dua lantai yang terletak di daerah Landungsari, Kota Malang. Tujuan dari penelitian ini yaitu untuk mengetahui hasil perbandingan dari metode konvensional dengan metode pracetak dalam aspek biaya maupun waktu untuk struktur balok dan kolom pada pembangunan rumah dua lantai. Metode pelaksanaan yang dipakai adalah metode konvensional dan metode pracetak (precast). Dilakukan analisis perhitungan biaya dan waktu dari metode konvensional dan pracetak untuk kemudian dilakukan perbandingan biaya dan waktu dari kedua metode tersebut. Diperoleh hasil perhitungan biaya dan waktu dari kedua metode tersebut yaitu metode konvensional membutuhkan biaya sebesar Rp 55.106.300,- dan waktu pelaksanaan selama 24 hari sedangkan metode pracetak membutuhkan biaya sebesar Rp 53.265.000,- dan waktu selama 9 hari. Dengan demikian metode pracetak lebih murah sebesar Rp 10.661.958,- bila dihitung dengan tambahan optimalisasi/hari dan lebih cepat 15 hari dibandingkan dengan metode konvensional.

Facial recognition technology has advanced significantly due to the development of deep learning algorithms. This paper explores deep learning, a branch of machine learning that employs multi-layered neural networks to simulate human decision-making processes in facial recognition. It provides a brief literature review of significant works in various deep learning architectures, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The core of the study is the implementation of the GhostFaceNets model, an enhancement of GhostNets, which is specifically designed for efficient and accurate facial recognition. By using Ghost Modules, this model reduces computational redundancy in generating additional feature maps through linear operations. An integrated attention mechanism is used in this study to emphasize critical facial features. Additionally, this study also employs the ArcFace loss function to improve class separation accuracy. The VGG2-FP dataset was used to train and evaluate this model and achieved an accuracy of 94.45%. This study contributes to the evolution of facial recognition systems, particularly in constrained computational environments.