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Microencapsulation of Phycocyanin from Spirulina platensis by Freeze-Drying: Optimization of Maltodextrin–Soy Protein Matrices for Enhanced Stability and Antioxidant Functionality
Marlina, Dian
; Saputra, Ryan Werytama
; Aji Prasetyo, Takad Bagas
; Muhamad Ansory, Hery
; Aisiyah, Siti
; Purwaningsih, Desi
Phycocyanin, a natural blue pigment from Spirulina platensis, exhibits strong antioxidant activity but is highly unstable under light, heat, and pH variations, limiting its practical applications. This experimental study addresses the lack of systematic optimization data on maltodextrin–soy protein isolate (SPI) wall matrices for phycocyanin microencapsulation via freeze-drying. Phycocyanin was extracted using phosphate buffer and encapsulated at different maltodextrin:SPI ratios (9:1, 8:2, and...
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In Vitro Degradation and Mechanical Performance of Mg AZ31B for Biodegradable Bone Implant Applications
Magnesium AZ31B is a promising biodegradable implant material due to its mechanical properties comparable to natural bone and its ability to degrade in physiological environments, potentially eliminating the need for secondary surgery. However, its rapid degradation can cause a significant loss of mechanical integrity, limiting its use in load-bearing applications. This study investigates the evolution of mechanical properties and surface characteristics of AZ31B during in-vitro immersion in Sim...
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Strategic Integration of Lean Construction into Green Building Regulations: A Factor-Based Assessment of Green Construction Indicators
The construction sector plays a critical role in sustainability through effective construction waste management. In Indonesia, green building practices are guided by the Green Construction Site Index (GCSI) and the Minister of Public Works and Housing Regulation No. 21 of 2021; however, their strategic alignment with Lean Construction principles remains limited. This study examines the strategic integration of Lean Construction into green building regulations by validating regulatory-based indic...
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Development and Performance Evaluation of a Micro-Scale RDF Briquette–Fueled Steam Power Prototype
Syarifah
; Latricia Aina Hidayat
; Rini Yunita Sari
; Maratis, Jerry
; Andri Krisna Hidayat
; Pawenary
; Hendri
Plastic waste is a major environmental problem in Indonesia due to its non-biodegradable nature. One innovative solution is converting waste into energy using Refuse-Derived Fuel (RDF) briquettes for small-scale power generation. This research designed and tested an RDF-based micro power plant prototype using briquettes composed of 80% dry organic biomass and 20% plastic for safe and stable combustion. The prototype consists of a combustion chamber, heat exchanger, impulse-type micro steam turbi...
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Attention-Augmented GRU for Stock Forecasting: A Trade-Off Between Directional Accuracy and Price Prediction Error
R. Daniel Hartanto
; Guruh Fajar Shidik
; Farrikh Alzami
; Ahmad Zainul Fanani
; Aris Marjuni
; Abdul Syukur
Journal of Computing Theories and Applications
Vol 3
, No 4
(2026)
Attention mechanisms have been widely incorporated into recurrent neural network architectures for financial time series forecasting, with most prior work reporting improvements in price-level error metrics. This study revisits that claim through a controlled empirical comparison of four deep learning architectures on nearly two decades of Telkom Indonesia (TLKM) closing price data from the Indonesia Stock Exchange (IDX). The models evaluated are a three-layer Gated Recurrent Unit (GRU) baseline...
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Behavioral Malware Detection via API Call Sequences: A Comparative Study of LSTM and Transformer Architectures Using NLP-Inspired Representations
Journal of Computing Theories and Applications
Vol 3
, No 4
(2026)
The increasing sophistication of malware has rendered traditional signature-based detection methods insufficient, necessitating behavior-driven and adaptive analytical frameworks. This study presents a sequential deep learning framework that models system-level API call sequences as structured linguistic representations for behavioral malware detection. Unlike conventional comparative studies, this work systematically evaluates recurrent and attention-based architectures under controlled experim...
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HUBUNGAN PEMBERIAN ASI EKSKLUSIF DAN MAKANAN PENDAMPING ASI DENGAN KEJADIAN STUNTING DI PERKOTAAN
Masitoh, Siti
; Syam, Heriza
; Jehanara, Jehanara
; Kusumastuti, Ani
; Debbiyantina, Debbiyantina
; Aulani, Fitria
Jurnal Kesehatan Masyarakat Indonesia
Vol 21
, No 1
(2026)
Latar Belakang: Masalah kesehatan gizi yang signifikan di Kota Serang adalah prevalensi stunting. Asupan gizi, khususnya ASI eksklusif dan makanan tambahan, berdampak signifikan terhadap pertumbuhan balita dan merupakan faktor penyebab stunting. Tujuan: Untuk menguji korelasi antara ASI eksklusif dan makanan pendamping, bersama dengan usia ibu, pendidikan, pekerjaan, dan paritas, dengan prevalensi stunting di wilayah Puskesmas Singandaru, Kota Serang. Metode: Penelitian ini menggunakan pendekata...
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ENVIRONMENTAL HEALTH RISK ASSESSMENT OF GROUNDWATER NITRATE AND NITRITE EXPOSURE AMONG FARMERS IN COASTAL AREA OF REMBANG, CENTRAL JAVA, INDONESIA
Irawati, Septiria
; Putri, Cita Fitria
; LPPM Universitas Diponegoro
; Gunungsari Village Government, Kaliori Subdistrict, Rembang Regency
Jurnal Kesehatan Masyarakat Indonesia
Vol 21
, No 1
(2026)
Background: The increasing use of fertilizers and pesticides in agricultural areas maycontaminate groundwater with nitrate and nitrite, posing health risks. Objective: This study aimed to assess the health risk from nitrate and nitrite exposure through groundwater consumption among farmers in the coastal area of Rembang, Central Java. Methods: A crosssectional study was conducted in Gunungsari Village, Kaliori Subdistrict. Groundwater samples from seven households were analyzed using spectrophot...
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Evaluating Civil Servant Selection through Machine Learning Analysis of National Insight, General Intelligence, and Personal Characteristics Test Scores
This study analyzes the score distribution of 2,490 candidates in the 2024 Ministry of Finance Public sector recruitment, focusing on the CNI, GIT, and PCT sections using machine learning classification. Models used include Logistic Regression (accuracy 0.7897), Random Forest (0.9779), and XGBoost (0.9809), all trained with default parameters (n_estimators=100, max_depth=None) and evaluated using accuracy, precision, recall, and F1-score. While ensemble models outperformed Logistic Regression, t...
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Viscosity Modeling of MES and SLS Using Machine Learning Method
Fathaddin, Muhammad Taufiq
; Setiati, Rini
; Akbar, Fahrurrozi
; Sumirat, Iwan
; Bharoto
; Ramadhan, Ranggi Sahmura
; Onnie Ridaliani Prapansya
; Ristawati, Arinda
Viscosity is crucial to improve the efficiency of injected fluids for oil displacement in reservoirs. Traditionally, research has focused on polymers that help reduce the mobility of injected fluids, while surfactant viscosity has received less consideration. This research investigated the viscosity behavior of methyl ester sulfonate (MES) and sodium lauryl sulfate (SLS) surfactant solutions using a machine learning method—adaptive neurofuzzy inference system (ANFIS). This study aimed to predict...
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