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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI BAREKENG: Jurnal Ilmu Matematika dan Terapan Indonesian Journal of Chemistry PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Jurnal Jamu Indonesia Jurnal Wasian
Syahrir, Nur Hilal A.
Department Of Statistics, Faculty Of Mathematics And Natural Sciences, Universitas Sulawesi Barat
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Target prediction of compounds on jamu formula using nearest profile method Nur Hilal A Syahrir; Sumarheni Sumarheni; Supri Bin Hj Amir; Hedi Kuswanto
Jurnal Matematika, Statistika dan Komputasi Vol. 17 No. 2 (2021): JANUARY 2021
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/jmsk.v17i2.11616

Abstract

Jamu is one of Indonesia's cultural heritage, which consists of several plants that have been practiced for centuries in Indonesian society to maintain health and treat diseases. One of the scientification efforts of Jamu to reveal its mechanism is to predict the target-protein of the active ingredients of the Jamu. In this study, the prediction of the target compound for Jamu was carried out using a supervised learning approach involving conventional medicinal compounds as training data. The method used in this study is the closest profile method adopted from the nearest neighbor algorithm. This method is implemented in drug compound data to construct a learning model. The AUC value for measuring performance of the three implemented models is 0.62 for the fixed compound model, 0.78 for the fixed target model, and 0.83 for the mixed model. The fixed compound model is then used to construct a prediction model on the herbal medicine data with an optimal threshold value of 0.91. The model produced 10 potential compounds in the herbal formula and its 44 unique protein targets. Even though it has many limitations in obtaining a good performance, the closest profile method can be used to predict the target of the herbal compound whose target is not yet known.
Efek Sinergis Bahan Aktif Tanaman Obat Berbasiskan Jejaring Dengan Protein Target Nur Hilal A. Syahrir; Farit Mochamad Afendi; Budi Susetyo
Jurnal Jamu Indonesia Vol. 1 No. 1 (2016): Jurnal Jamu Indonesia
Publisher : Tropical Biopharmaca Research Center, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1489.882 KB) | DOI: 10.29244/jji.v1i1.6

Abstract

Medicinal plants contain inherently active ingredients. Such ingredients are beneficial to prevent and cure diseases, as well as to perform specific biological functions. In contrast to synthetic drugs, which is based on one single chemicals, medicinal plants exert their beneficial effects through the additive or synergistic action of several chemical compounds. Those chemical compound act on single or multiple targets (multicomponent therapeutic) associated with a physiological process. Active ingredients combinations show a synergistic effect. This means that the combinational effect of several active ingredients is greater than that of individual one acting separately. A network target can be used to identify synergistic effects of plants active ingredients. The method of NIMS (Network target-based Identification of Multicomponent Synergy) is a computational approach to identify the potential synergistics effect of active ingredients. It also assessess synergistic strength of any active ingradients at the molecular level by synergy scores. We investigate these synergistic on a Jamu formula for diabetes mellitus type 2. The Jamu formula is composed of four medicinal plants, namely Tinospora crispa , Zingiber officinale, Momordica charantia, and Blumea balsamivera. Our work succesfully demonstrates that the highest synergy scores on medicinal plants synergy can be seen in pairs of several active ingredients in Zingiber officinale. On the other hand, the synergy of pairs of active ingredients in Momordica charantia and Zingiber officinale posseses a relatively high score. The same occurs in Tinospora crispa and Zingiber officinale.
Will Indonesia's Forests Survive Development Pressure? Machine Learning Predictions for Energy-Critical Tropical Watersheds Utami A, Widyanti; Irlan, Irlan; Syahrir, Nur Hilal A; Rosmaeni, Rosmaeni
Jurnal Wasian Vol. 12 No. 01 (2025): June
Publisher : Forestry Department, University of Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62142/hjs6a555

Abstract

Land Use and Land Cover (LULC) changes play an important role in influencing the hydrological conditions of a watershed. The conversion of land such as forests, shrubs and grasslands into agricultural land can disrupt the hydrological balance of the watershed. The availability of information related to LULC dynamics in the future is needed to assist sustainable watershed management planning. Machine learning technology, such as Cellular Automata, can provide accurate predicting. The objective of this research is to simulate LULC based on machine learning in the Mamasa Sub-watershed. Two model combinations were employed to simulate LULC: Artificial Neural Network-Cellular Automata (ANN-CA) and Logistic Regression-Cellular Automata (LR-CA). The research results found that the ANN-CA model achieved percent of correctness and overall kappa of 83.6745 and 0.75412, respectively, which were higher than those of the LR-CA model (82.3498 and 0.73361). The prediction results of both model combinations still fall below the actual LULC values, especially in the case of large LULC classes such as forests, range-shrub, rice, and pasture. Conversely, higher accuracy is observed for smaller classes such as wetlands-forested, orchard, residential, and oak. However, it should be noted that this research did not include several socio-economic variables, such as population and income level, which are considered to influence changes in LULC. Future research is expected to analyse the influence of each variable and include some socio-economic variables that may have a significant influence on LULC change.
AGGLOMERATIVE HIERARCHICAL CLUSTERING ANALYSIS IN PREDICTING ANTIBACTERIAL ACTIVITY OF COMPOUND BASED ON CHEMICAL STRUCTURE SIMILARITY Siswanto, Siswanto; Syahrir, Nur Hilal A
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (490.231 KB) | DOI: 10.30598/barekengvol16iss4pp1441-1452

Abstract

Resistance to antibiotics is increasing to alarmingly high levels. As antibiotics are less effective, more infections are becoming more complex and often impossible to treat. Numerous antibiotics discovered in marine organisms show that the marine environment, which accounts for over half of the world's biodiversity, is a massive source for novel antibiotics and that this resource must be explored to identify next-generation antibiotics. This research aimed to predict antibacterial activity in marine compounds using a computational approach to reduce the cost and time of finding marine organisms, extracting, and testing numerous unknown marine compounds' bioactivities. We used a simple unsupervised learning approach to predict the biological activity of marine compounds using agglomerative hierarchical clustering. We mixed antibiotic drug data in DrugBank Database and chemical compound data from marine organisms in literature to compile our dataset. We applied five linkage methods in our dataset and compared the best method by assessing internal validation measurement. We found that the Ward with squared dissimilarity matrix is the best method in the dataset, and ten compounds from 73 compounds of the marine compound are determined as potential marine compounds which have antibacterial activity.
Toxicity and α-Amylase Inhibitory Potential of Tagetes erecta Leaf Extract: In Vitro and In Silico Approaches Rasyid, Herlina; Soekamto, Nunuk Hariani; Musa, Bulkis; Siswanto, Siswanto; Labanni, Arniati; Suma, Artania Adnin Tri; Syahrir, Nur Hilal A; Bahrun, Bahrun; Badrawati, Kadek Susi; Yusuf, Mohammad Taufik
Indonesian Journal of Chemistry Vol 25, No 5 (2025)
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijc.104489

Abstract

Tagetes erecta is one of traditional herbs with a variety of pharmacological actions. This study attempted to assess the toxicity and antidiabetic activity of T. erecta leaf extract. The extraction was carried out by maceration, then continued with phytochemical analysis. Toxicity of the extract was conducted using the brine shrimp lethality test. The antidiabetic activity was evaluated by α-amylase inhibitory using the 3,5-dinitrosalicylic acid method. The phytochemical of the most active extract was identified using GC-MS and subjected to bind the α-amylase (PDB ID: 2QV4) employing molecular docking. The LC50 values of n-hexane, EtOAc, and MeOH extracts were 33.41, 14.00, and 35.03 ppm, respectively, indicating high toxicity. The antidiabetic activity showed that EtOAc extract has the lowest IC50 value (1053.95 mg/L). Molecular docking analysis revealed the compounds 1–5 has range of binding energy at −4.07 to −4.83 kcal/mol. Acarbose as a positive control showed the lower binding energy at −5.03 kcal/mol, indicated more effective α-amylase inhibitory. This study revealed that T. erecta leaf extract has significant cytotoxic potential, which may warrant further exploration for anticancer applications. However, the relatively weak α-amylase inhibitory and lower binding affinity compared to acarbose imply limited utility as an antidiabetic agent.
Pelatihan Pembuatan Sistem Informasi Kelurahan untuk Meningkatkan Layanan Publik di Kelurahan Anreapi, Polman, Sulawesi Barat: Workshop in Providing Kelurahan Information System to Enhance Public Services in Anreapi, Polman, Sulawesi Barat Seppewali, Andi; Syahrir, Nur Hilal A
PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Vol. 8 No. 6 (2023): PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/pengabdianmu.v8i6.5569

Abstract

Kelurahan is a form of local government in Indonesia which act as a servant in sub-district level. As an administrative unit of government, especially for the sub-district level, Kelurahan is responsible for providing various public services which are directly useful to the local community. One of the public services provided by the kelurahan includes residential administration. Our PkM team conducted workshop in establishing information systems to improve public services in Kelurahan Anreapi, Polman, Sulawesi Barat. The activity consists of three stages. The first stage is the preparation of workshop, ie. Searching platforms for information system, registrating the domain, and creating the website. The second stage is the workshop activity, and the third is evaluation of workshop’s activity. This activity significantly impacts accelerating participants' knowledge and skills regarding the information systems.
Co-Authors Artania Adnin Tri Suma Badrawati, Kadek Susi Bahrun Bahrun Budi Susetyo Farit Mochamad Afendi Hedi Kuswanto Irlan Labanni, Arniati Musa, Bulkis Nunuk Hariani Soekamto Rasyid, Herlina rosmaeni, rosmaeni S Siswanto Seppewali, Andi Sumarheni Sumarheni Supri Bin Hj Amir Utami A, Widyanti Yusuf, Mohammad Taufik