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Structure Relaxation Disruption on Temperature-dependence of Polymerization of HTPB-based Polyurethane Restasari, Afni; Hamid, Nur; Marpaung, Leonard; Rusnaenah, Andi; Sukma, Adi; Sukma, Rahmawati
Indonesian Journal of Aerospace Vol. 19 No. 2 (2021)
Publisher : BRIN Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.jtd.2021.v19.a3654

Abstract

The temperature-dependence of polymerization rate of hydroxyl-terminated polybutadiene (HTPB)-based polyurethane can be disrupted by a structure relaxation of polymer. Objective of the study is to investigate the disruption on the polyurethane (PU) formed of various molecular weight of HTPB. The study was carried out by applying temperature of 50, 60 and 70 oC in measuring viscosity until 80 minutes of reaction. The sample that were used is HTPB with various molecular weight and Toluene diisocyanate (TDI). Based on decreasing value of viscosity, it is obtained that relaxation temperature of HTPB-based PU is around 60 – 70 oC. By applying Eyring equation of flow, it is found that relaxation of structure causes the existence of relaxation dominant-time (RDT). RDT is the reaction time at which molar volume reaches the maximum value. Furthermore, by determining activation entropy, the RDT was revealed to be a borderline between two type of polymerization. Linear reaction occurs before RDT, while cross-link reaction occurs after RDT. From structure point of view, PU-polymerization type of HTPB with low molecular weight tend to be more sensitive towards structure relaxation which is originated from hard segment.
Vote Detection on Ballots Using Thresholding and Centroid Detection Techniques Qadriah, Lailatul; Hamid, Nur
SITEKIN: Jurnal Sains, Teknologi dan Industri Vol 23, No 1 (2025): December 2025
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/sitekin.v23i1.38937

Abstract

General elections are an agenda carried out to elect and determine leaders in each region. One of the important stages in the general election process is the vote-counting stage. This study aims to implement several digital image processing methods. Digital image processing plays an important role in the automatic reading of ballot papers to increase the speed of the vote-counting process. In this study, the process of reading ballot images was conducted to produce numerical data based on the coordinates of specific parts of the image. Image processing was performed using GNU Octave software, which is simple yet effective in detecting votes on ballot papers and converting them into numerical data based on centroid coordinates. This method has advantages in terms of implementation simplicity and computational efficiency. The main stages of this study include image conversion to grayscale, thresholding, black pixel detection, segmentation, centroid coordinate detection of punched ballot marks, and conversion into numerical form. In this study, 47 ballot image samples were used. The results of this study show that this method can achieve an accuracy rate of 78.7%.
Adawiyah, Rabiatul; Hamid, Nur; Sa’diyah, Sa’diyah; Aroyandini, Elvara Norma; Kholis, Nur; Mukti, Beta Pujangga
Edukasia : Jurnal Penelitian Pendidikan Islam Vol 19, No 1 (2024): EDUKASIA
Publisher : Program Studi Pendidikan Agama Islam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21043/edukasia.v19i1.20930

Abstract

Evaluating Lexicon Weighting and Machine Learning Models for Sentiment Classification of Indonesian Mangrove Ecotourism Reviews Chahyadi, Ferdi; Uperiati, Alena; Pratiwi , Risdy Absari Indah; Hamid, Nur
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5563

Abstract

Sentiment analysis on ecotourism reviews presents specific challenges due to descriptive writing styles, the use of ambiguous words, and contextual meaning shifts (contextual polarity shift). These characteristics often cause lexicon-based approaches to produce unstable polarity labels. This study aims to evaluate the influence of two lexicon weighting methods, namely Mean Weighting and Summation Weighting, on the initial sentiment labeling of mangrove ecotourism reviews and to assess the performance of machine learning models trained using these labels. The research method includes text preprocessing, lexicon-based scoring using the InSet lexicon, feature extraction with Term Frequency–Inverse Document Frequency (TF–IDF), and the training of two classification algorithms, Support Vector Machine (SVM) and Logistic Regression (LR). The results show that the Mean Weighting method produces more stable polarity scores and higher model performance. The combination of SVM with Mean Weighting achieves the best results with an accuracy of 0.902, macro precision of 0.876, macro recall of 0.819, a macro F1-score of 0.841, and a weighted F1-score of 0.899. Meanwhile, LR with Mean Weighting reaches an accuracy of 0.891 with a similar performance pattern. In contrast, the Summation Weighting method results in lower performance for both algorithms. Error analysis indicates that neutral sentences and ambiguous words such as “bagus” and “ramai” frequently lead to misclassification. These findings highlight that the choice of lexicon weighting method plays a crucial role in improving sentiment classification accuracy and contributes to the development of hybrid approaches in text mining and sentiment analysis for the Indonesian language.