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ILham PENGAMATAN POHON TIDUR SIAMANG DAN GIBBON DI STASIUN PENELITIAN SORAYA SUBULUSSALAM: OBSERVATION OF SIAMANG AND GIBBON SLEEPING TREES AT SORAYA SUBULUSSALAM RESEARCH STATION) amar, aidil; Reza Ilham Akbar; Syifa Saputra; Reza Fahmi; Munawar
Jurnal Lingkungan Almuslim Vol 3 No 2 (2024): Jurnal Lingkungan Almuslim
Publisher : Program Studi Magister Pengelolaan Sumberdaya Alam dan Lingkungan Universitas Almuslim

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Abstract, Leuser Management is within the Leuser Ecosystem Area but outside the Leuser National Ecosystem Park. The first research station is Ketambe in the southeastern part and the second research station is Suaq Balimbing in the southern part of Aceh, both of which are in the Leuser Ecosystem Area and are also in the Gunung Leuser National Park (Leuser Management Unit, 1997). Research objectives: To identify the types of sleeping trees chosen by gibbons and gibbons as sleeping places and to analyze environmental factors that influence the choice of these trees as well as observing the sleeping behavior patterns of these two primate species. Results and discussion Daily roaming distance of Siamang Siamang Sleeping Tree Position at the Soraya Research Station The Soraya Research Station in Subussalam, Aceh, is an important location for the study of gibbons and their ecology in their natural habitat. The sleeping gibbon trees at this research station may be a major focus in understanding gibbon behavior and ecology in the region. On the Soraya track trail, shown in Figure IV.1, the yellow track is the waypoint for the siamang as well as the position of the sleeping gibbon tree. In this research, there were 12 positions/points of gibbon sleeping trees at the Soraya research station. The following is a track image of the position of the gibbon tree sleeping during the research, which can be seen in Figure IV.1.

Pengaruh Lama Pengomposan Jerami Padi Sebagai Bahan Pembawa Terhadap Populasi Azospirillum sp Fauza, Saniar; Munawar; Marlina
Jurnal Sains Pertanian Vol. 9 No. 1: February, 2025
Publisher : Fakultas Pertanian Universitas Almuslim Bireuen Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/jsp.v9i1.3061

Bakteri Azospirillum sp, merupakan salah satu bakteri penambat nitrogen bebas yang bersifat non simbiotik, bakteri ini hidup di daerah perakaran tanaman dan berkembang biak membentuk koloni terutama pada daerah perpanjangan akar dan pangkal bulu akar. Bakteri ini dapat dimanfaatkan sebagai pupuk hayati yang membantu penambatan nitrogen di udara. Salah satu faktor yang menentukan keberhasilan aplikasi bakteri pemfikasi nitrogen adalah bahan pembawa atau carrier. Bahan pembawa berperan menjaga viabilitas dan efektivitas mikroba dalam pupuk hayati sebelum diaplikasikan. Bahan pembawa yang sering digunakan yaitu kompos. Tujuan penelitian ini untuk mengetahui pengaruh lama pengomposan jerami padi terhadap populasi bakteri Azospirillum sp. Rancangan yang digunakan dalam penelitian ini adalah Rancangan Acak Kelompok (RAK) yang terdiri dari empat perlakuan yaitu lama pengomposan jerami padi 1 minggu, 2 minggu, 3 minggu dan 4 minggu. Hasil penelitian menunjukkan bahwa perlakuan lama pengomposan jerami padi satu minggu menghasilkan jumlah populasi tertinggi yaitu 21,13x106 CFU/g, dan Kandungan C/N terbaik 25,11 yaitu pada perlakuan dengan lama pengomposan 4 minggu.

Inadequate Preoperative Assessment and Its Clinicopathological Correlates in Patients Referred for Completion Thyroidectomy: A Tertiary Referral Center Analysis Munawar; R Maman Abdurrahman
Bioscientia Medicina : Journal of Biomedicine and Translational Research Vol. 10 No. 2 (2025): Bioscientia Medicina: Journal of Biomedicine & Translational Research
Publisher : HM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/bsm.v10i2.1497

Background: Completion thyroidectomy (CT) for differentiated thyroid carcinoma (DTC) is a high-risk procedure, frequently performed following an oncologically incomplete primary operation. This study characterizes the preoperative diagnostic assessment deficiencies in a cohort of DTC patients referred to a tertiary center for re-operation and identifies factors associated with residual disease. Methods: We conducted a retrospective, single-center analysis of all patients who underwent CT for DTC at Hasan Sadikin General Hospital, Indonesia, over a 30-month period (January 1st, 2023, to June 30th, 2025). Data on preoperative assessments at the referring hospitals (ultrasonography (US) quality, fine-needle aspiration biopsy (FNAB), hormonal tests), primary surgical indications, and clinicopathological outcomes from both operations were extracted and analyzed using descriptive and bivariate statistics (Fisher's Exact Test). Results: A total of 27 patients met the inclusion criteria. Analysis of their initial workup revealed significant omissions: 14/27 (51.9%) lacked FNAB, and 5/27 (18.5%) lacked hormonal testing. While 24/27 (88.9%) underwent a primary US, only 20.8% of these reports (5/24) were ATA-compliant staging examinations. Only 5/27 patients (18.5%) received a complete trimodal assessment. Upon re-operation, 10/27 (37.0%) had residual carcinoma. This finding was significantly associated with the omission of primary FNAB (57.1% vs. 15.4%, p = 0.027). Conclusion: In this cohort of referred patients, incomplete preoperative assessment was nearly universal and strongly associated with adverse pathological findings. These data highlight the urgent need for standardized, evidence-based preoperative protocols and strengthened referral systems to ensure patients receive the correct primary operation.

Inadequate Preoperative Assessment and Its Clinicopathological Correlates in Patients Referred for Completion Thyroidectomy: A Tertiary Referral Center Analysis Munawar; R Maman Abdurrahman
Bioscientia Medicina : Journal of Biomedicine and Translational Research Vol. 10 No. 2 (2025): Bioscientia Medicina: Journal of Biomedicine & Translational Research
Publisher : HM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/bsm.v10i2.1497

Background: Completion thyroidectomy (CT) for differentiated thyroid carcinoma (DTC) is a high-risk procedure, frequently performed following an oncologically incomplete primary operation. This study characterizes the preoperative diagnostic assessment deficiencies in a cohort of DTC patients referred to a tertiary center for re-operation and identifies factors associated with residual disease. Methods: We conducted a retrospective, single-center analysis of all patients who underwent CT for DTC at Hasan Sadikin General Hospital, Indonesia, over a 30-month period (January 1st, 2023, to June 30th, 2025). Data on preoperative assessments at the referring hospitals (ultrasonography (US) quality, fine-needle aspiration biopsy (FNAB), hormonal tests), primary surgical indications, and clinicopathological outcomes from both operations were extracted and analyzed using descriptive and bivariate statistics (Fisher's Exact Test). Results: A total of 27 patients met the inclusion criteria. Analysis of their initial workup revealed significant omissions: 14/27 (51.9%) lacked FNAB, and 5/27 (18.5%) lacked hormonal testing. While 24/27 (88.9%) underwent a primary US, only 20.8% of these reports (5/24) were ATA-compliant staging examinations. Only 5/27 patients (18.5%) received a complete trimodal assessment. Upon re-operation, 10/27 (37.0%) had residual carcinoma. This finding was significantly associated with the omission of primary FNAB (57.1% vs. 15.4%, p = 0.027). Conclusion: In this cohort of referred patients, incomplete preoperative assessment was nearly universal and strongly associated with adverse pathological findings. These data highlight the urgent need for standardized, evidence-based preoperative protocols and strengthened referral systems to ensure patients receive the correct primary operation.

Penerapan Analisis Asosiasi Untuk Mengetahui Pola Pembicaraan Depresi Pada X Rifqi Adi Prasetya; Munawar; Habibullah Akbar; Popong Setiawati
Paradigma: Jurnal Filsafat, Sains, Teknologi, dan Sosial Budaya Vol. 31 No. 2 (2025): Paradigma: Jurnal Filsafat, Sains, Teknologi, dan Sosial Budaya
Publisher : Universitas Insan Budi Utomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33503/paradigma.v31i2.2557

Penelitian ini bertujuan untuk mengidentifikasi pola pembicaraan yang mencerminkan gejala depresi pada media sosial X dengan menerapkan metode association rule mining. Dengan meningkatnya penggunaan media sosial sebagai wadah ekspresi emosional, studi ini berupaya mengungkap hubungan antar kata yang sering muncul bersamaan dalam konteks depresi. Penelitian ini menggunakan pendekatan Knowledge Discovery in Database (KDD) yang mencakup tahapan seleksi data, pre-processing, transformasi data, data mining, interpretasi hasil, dan validasi pakar. Data dikumpulkan melalui tools Tweet Harvest dengan kata kunci seperti “capek”, “sedih”, “stress”, “sengsara”, “lelah”, “gelisah” dan “putus asa”, menghasilkan 21.020 tweet, yang kemudian diproses dan dianalisis menggunakan algoritma Apriori dan FP-Growth. Hasilnya menunjukkan 12 aturan asosiasi yang menggambarkan ekspresi emosi negatif dengan intensitas tinggi, seperti asosiasi antara “hidup” dan “sengsara” serta “sedih” dan “banget”, yang mencerminkan fokus pada diri sendiri, kelelahan emosional, dan persepsi negatif terhadap hidup sebagai indikasi umum dari depresi. Validasi pakar mengonfirmasi bahwa pola-pola tersebut memiliki relevansi klinis. Apriori terbukti lebih efisien dari segi waktu dan penggunaan memori dibanding FP-Growth. Temuan ini menunjukkan bahwa pola bahasa di media sosial dapat menjadi indikator dini gejala depresi.

Sentiment Analysis of Marketplace Application Reviews Using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) Arief Ichwani; Munawar; Rilla Gantino
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 7 No. 2 (2024): Jurnal Teknologi dan Open Source, December 2024
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v7i2.4972

Shopee is one of the most popular online marketplaces in Indonesia, with more than 103 million users in 2023. Most users consider factors such as customer reviews, ratings, prices, and free shipping promotions before making a purchase. Analyzing user reviews is essential to understand consumer perceptions of services, identify satisfaction or dissatisfaction, and detect potential issues that need to be addressed. However, sentiment analysis faces challenges in processing text with diverse language styles, structures, and informal expressions. To overcome these challenges, this study applies machine learning algorithms—Support Vector Machine (SVM) and K-Nearest Neighbors (KNN)—for classifying sentiment in Shopee user reviews. Data labeling using the Lexicon InSet method produced 9,509 positive reviews (47.55%), 7,686 negative reviews (38.43%), and 2,805 neutral reviews (14.03%). Based on the Confusion Matrix results, SVM outperformed KNN, particularly in classifying negative and neutral sentiments with higher accuracy. These findings indicate that SVM is a more effective and efficient model for sentiment analysis of user reviews on the Shopee platform.

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