Article Per Year (5 Year)
p-Index From 2021 - 2026
4.445
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management Jurnal Ilmu Pertanian Indonesia Jurnal Silvikultur Tropika (JST) MEDIA KONSERVASI FORESTA BIOTROPIA - The Southeast Asian Journal of Tropical Biology TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Penelitian Hutan Tanaman Indonesian Journal of Agricultural Science Konversi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Jurnal Tataloka JURNAL KONVERSI MAJALAH ILMIAH GLOBE SEJ (Science Education Journal) BAREKENG: Jurnal Ilmu Matematika dan Terapan LIMNOTEK - Perairan Darat Tropis di Indonesia International Journal of Remote Sensing and Earth Sciences (IJReSES) JURNAL METEOROLOGI DAN GEOFISIKA Journal of Sylva Indonesiana Indonesian Journal of Electrical Engineering and Computer Science Jurnal Hutan Tropika Jurnal Tanah dan Iklim Jurnal Risalah Kebijakan Pertanian dan Lingkungan International Journal of Electronics and Communications Systems JURNAL HUTAN TROPIS Jurnal Informatika dan Teknologi Informasi Policy Brief Pertanian, Kelautan, dan Biosains Tropika The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Lailan Syaufina
Departemen Silvikultur, Fakultas Kehutanan, Institut Pertanian Bogor, Kampus IPB Dramaga Bogor 16680
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation Veterinary

Published : 74 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 74 Documents
Search

 4   5   6   7   8 
SIPP KARHUTLA: Implikasi Pembaharuan Kebijakan Syaufina, Lailan; Sitanggang, Imas Sukaesih; Purwanti , Endang Yuni
Policy Brief Pertanian, Kelautan, dan Biosains Tropika Vol 4 No 2 (2022): Policy Brief Pertanian, Kelautan dan Biosains Tropika
Publisher : Direktorat Kajian Strategis dan Reputasi Akademik IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/agro-maritim.0402.253-257

Abstract

Kebakaran hutan dan lahan (karhutla) di Indonesia terjadi setiap tahun dan memberikan dampak yang merugikan pada beberapa aspek kehidupan dan lingkungan. Patroli pencegahan karhutla merupakan kegiatan untuk mengobservasi kondisi rawan karhutla di lapangan, mendeteksi dini terjadinya kebakaran, dan melakukan sosialisasi pada stakeholder terkait. Pengembangan Sistem Informasi Patroli Pencegahan (SIPP) Karhutla bertujuan untuk memudahkan tim patroli pencegahan karhutla dan pengelola data patroli melaksanakan tugasnya dengan lebih efektif dan efisien. Sejak tahun 2021, SIPP Karhutla telah diterapkan oleh Manggala Agni di wilayah Sumatera dan telah memiliki payung hukum berupa Perdirjen Pengendalian Perubahan Iklim Kementerian Lingkungan Hidup dan Kehutanan ( KLHKKLHK). Kebijakan ini telah terbukti mendukung pelaksanaan patroli secara lebih efisien dan efektif efektif. Beberapa implikasi kebijakan tersebut telah teridentifikasi untuk disiapkan oleh KLHK.
Analisis Diskursus Pengendalian Kebakaran Hutan dan Lahan di Indonesia Nadhifah, Putri Addini Arsya; Syaufina, Lailan; Ekayani, Meti; Erbaugh, James Thomas
Journal of Tropical Silviculture Vol. 15 No. 02 (2024): Jurnal Silvikutur Tropika
Publisher : Departemen Silvikultur, Fakultas Kehutanan dan Lingkungan, Institut Pertanian Bogor (IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/j-siltrop.15.02.177-184

Abstract

Sektor pertanian, kehutanan dan penggunaan lahan (AFOLU) merupakan sektor terbesar penyumbang emisi gas rumah kaca di Indonesia. Dalam sector ini, kebakaran hutan dan lahan gambut memberikan dampak yang besar. Optimalisasi pengendalian kebakaran perlu melibatkan berbagai pihak dan harusnya menjadi solusi terhadap penyebab kebakaran. Media ilmiah nasional mampu memberikan wawasan mengenai aktor-aktor penting, potensi penyebab, dan implementasi pengendalian kebakaran hutan di tingkat nasional dan lokal. Penelitian ini menggunakan analisis diskursus yang bertujuan menganalisis peran media ilmiah dalam memberikan informasi mengenai fenomena kebakaran hutan dan segala bentuk aktivitas pengendalian kebakaran. Hasil analisis menunjukan adanya gap atau ketidaksejalanan antara tren artikel ilmiah dengan data tren kebakaran. Selain itu, ditemukan bias antara penyebab dan upaya pengendalian kebakaran hutan. Diskursus jurnal nasional menunjukan upaya pengendalian kebakaran hutan dan lahan di Indonesia lebih berorientasi pada aspek ekologis dan belum memperhatikan aspek ekonomi, padahal faktor ekonomi dinyatakan paling banyak sebagai faktor penyebab kebakaran. Kata kunci: diskursus, kebakaran hutan, pengendalian, media ilmiah
Application of Random Forest Algorithm to Analyze the Confidence Level of Forest Fire Hotspots in Riau Peatland Unik, Mitra; Sukaesih Sitanggang, Imas; Syaufina, Lailan; Surati Jaya, I Nengah
Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Vol 15 No 2 (2025): Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (JPSL)
Publisher : Pusat Penelitian Lingkungan Hidup, IPB (PPLH-IPB) dan Program Studi Pengelolaan Sumberdaya Alam dan Lingkungan, IPB (PS. PSL, SPs. IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jpsl.15.2.255

Abstract

Forest fires pose a significant challenge in Riau Province, Indonesia, especially in peatland areas. This study employs the Random Forest (RF) algorithm to analyze the confidence levels of hotspots, aiming to predict potential fire occurrences and improve fire management strategies. The research focuses on peatlands spanning 3.86 million ha, using key variables such as NDVI, surface temperature, and peat thickness derived from satellite data. The model achieved an average AUC of 0.732 and a classification accuracy of 70.3%, with medium-confidence hotspots demonstrating the best predictive performance (AUC: 0.707, F1-score: 0.804). However, the model struggled with low-confidence hotspots, reflecting challenges in distinguishing less prominent patterns in the data. Compared to other methods, RF demonstrates strong potential in handling complex environmental datasets, making it a valuable tool for hotspot prediction. This study contributes to understanding forest fire risks in peatlands and provides actionable insights for improving preparedness and mitigation efforts.
Exploration of Data Handling Techniques to Improve PM2.5 Prediction Using Machine Learning Unik, Mitra; Sitanggang, Imas Sukaesih; Syaufina, Lailan; Jaya, I Nengah Surati
International Journal of Electronics and Communications Systems Vol. 5 No. 1 (2025): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v5i1.25687

Abstract

Particulate matter (PM₂.₅) is one of the most dangerous air pollutants because it can penetrate the respiratory system and cause serious health problems. Amidst the limitations of a real-time and comprehensive air quality monitoring system, a data-driven predictive approach is needed that can accurately project PM₂.₅ concentrations. This study aims to develop a PM₂ concentration prediction model using the Random Forest Regressor (RFR) algorithm optimised through a series of data pre-processing techniques. The pre-processing techniques include outlier detection with four methods (Isolation Forest, Autoencoder ANN, OCSVM, IQR) and missing value handling using three approaches (Spline Cubic Interpolation, Nearest Point Interpolation, Data Removal). The daily data used covered 12 environmental variables (including rainfall, temperature, relative humidity, AOD, and NDVI) from the period of March 2022 to March 2023, with PM₂.₅ as the target. The RFR model was built with 100 decision trees and 10-fold cross-validation to improve accuracy. Results showed the combination of IQR (outlier detection) and data deletion (missing values) produced the best performance with RMSE 0.082, MAE 0.027, and R² 0.886. The most influential variables were temperature (TEMP), relative humidity (RHU), and evapotranspiration (ET). This research contributes to the development of an accurate air quality prediction model, supporting the mitigation of PM₂.₅ pollution impacts on public health
SOIL MOISTURE PREDICTION MODEL IN PEATLAND USING RANDOM FOREST REGRESSOR Taihuttu, Helda Yunita; Sitanggang, Imas Sukaesih; Syaufina, Lailan
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss4pp2505-2516

Abstract

Soil moisture is one of the factors that has recently become the focus of research because it is strongly correlated with forest and land fires, where low soil moisture will increase drought and the incidence of forest and land fires. For this reason, this study aims to create a prediction model for soil moisture as an early prevention of fires in peatlands using the Random Forest Regressor (RFR) algorithm. RFR is used because of its ability to predict values and its resistance to overfitting and outliers. A dataset covering soil moisture, precipitation, temperature, maturity, and peat thickness was collected from August 2019 to December 2023. The data includes soil moisture, precipitation, temperature, maturity, and peat thickness. The data were divided into 80% for modeling and 20% for testing. Model performance was optimized through random search CV, resulting in significant prediction accuracy R-squared: 0.914, MAE: 0.0081, MSE: 0.0007, RMSE: 0 .0271, and MAPE: 0.969. These findings demonstrate the effectiveness of RFR in soil moisture prediction and pave the way for more appropriate and timelier implementation of fire mitigation strategies.
Analisis Dampak Kabut Asap dari Kebakaran Hutan dan Lahan dengan Pendekatan Text Mining Efendi, Zuliar; Sitanggang, Imas Sukaesih; Syaufina, Lailan
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 5: Oktober 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023107248

Abstract

Kebakaran hutan dan lahan (karhutla) berdampak buruk bagi lingkungan serta ekosistem. Kabut asap merupakan salah satu akibat yang ditimbulkan dari kebakaran hutan dan lahan. Keresahan dari munculnya kabut asap dan kebakaran hutan menjadi trending topic pada media sosial Twitter. Analisis Twitter perlu dilakukan untuk melihat kesesuaian hashtag yang digunakan dengan topik yang dibahas yaitu kabut asap. Data Twitter dapat dianalisis menggunakan text mining. Penelitian ini bertujuan untuk melihat hubungan antara percakapan di media sosial Twitter dengan kejadian kabut asap yang muncul dari kebakaran hutan dan lahan. Metode yang digunakan adalah teknik text mining yaitu menggunakan algoritme clustering. Data yang digunakan adalah data tweet terkait kabut asap di Provinsi Riau pada jarak 11 – 17 September 2019 dan juga data hotspot atau titik panas serta citra Sentinel2. Data tweet dikelompokkan dengan beberapa percobaan pada jarak antar cluster yaitu single linkage, complete linkage, average linkage, dan ward. Hasil clustering menunjukkan bahwa validitas cluster tertinggi memiliki silhouette index sebesar, 0,3360 dengan jarak antar cluster menggunakan ward. Hasil cluster menunjukkan bahwa terdapat tiga cluster yang dominan pembahasannya terkait kabut asap. Data Twitter pada ketiga cluster tersebut memiliki ciri istilah atau term yang berkaitan dengan kabut asap antara lain "kabut", "asap", dan "udara". terdapat di wilayah Pekanbaru serta wilayah Bengkalis, Provinsi Riau. Hasil dapat menjadi salah satu cara pengendalian karhutla yaitu deteksi dini dengan menggunakan media sosial Twitter.   Abstract  Forest and land fires have a harmful impact on the environment and ecosystem. Haze is one of the consequences that arise from forest fires and the environment. Anxiety about haze and forest fires is a trending topic on social media Twitter. Twitter analysis needs to be done to see the compatibility of the hashtags used with the haze topic. The Twitter data can be analyzed using text mining. This study aims to see the relation between conversations on social media Twitter and the occurrence of haze that arises from forest and land fires. The method used is a text mining technique that uses a clustering algorithm. The data used are tweet data related to haze in Riau Province in the range 11-17 September 2019 as well as hotspot data and Sentinel-2 imagery. Tweet data were clustered by several experiments on the distance between clusters, namely single linkage, complete linkage, average linkage, and ward. Clustering results show that the highest cluster validity has a silhouette index of 0.3360 with the distance between clusters using wards. The cluster results show that there are three clusters that are dominant in the discussion related to haze. The Twitter data for the three clusters has the characteristics of terms related to smog, including "kabut", "asap", and "udara". The impact felt by the people of Riau Province through social media Twitter related to the haze is the impact on health and air quality. Cluster tweets that discuss the topic of forest and land fires and haze are in the Pekanbaru and Bengkalis regions, Riau Province. The results can be one of the karhutla controls is early detection by using social media Twitter.
Strategi Pengembangan Ekowisata Berkelanjutan di Kawasan Mangrove Pangkal Babu Tanjung Jabung Barat Wulandari, Ratu Mutiara; Putra, Aditya Handoyo; Syaufina, Lailan
Policy Brief Pertanian, Kelautan, dan Biosains Tropika Vol. 7 No. 3 (2025): Policy Brief Pertanian, Kelautan, dan Biosains Tropika
Publisher : Direktorat Kajian Strategis dan Reputasi Akademik IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/agro-maritim.0703.1372-1377

Abstract

Ekosistem mangrove Pangkal Babu di Kabupaten Tanjung Jabung Barat, Jambi, memiliki potensi besar dalam pengembangan ekowisata berkelanjutan yang mengintegrasikan konservasi lingkungan, pemberdayaan masyarakat, dan mitigasi perubahan iklim. Meskipun pernah mengalami degradasi akibat alih fungsi lahan menjadi tambak dan perkebunan, rehabilitasi berbasis masyarakat berhasil memulihkan tutupan mangrove dan melahirkan destinasi ekowisata nasional sejak 2019. Pengembangan kawasan ini tidak hanya menjaga jasa ekosistem mangrove seperti perlindungan pesisir, penyediaan sumber pangan, serta penyerapan karbon tetapi juga membuka peluang ekonomi kreatif dan pariwisata. Namun saat ini pengembangan pariwisata masih terbatas, terdapat beberapa kendala diantaranya keterbatasan infrastruktur, kapasitas pengelolaan, dan promosi masih menjadi hambatan utama. Kebijakan strategis diperlukan dengan menekankan pada konservasi dan rehabilitasi kawasan multipihak, penguatan kelembagaan Pokdarwis dan BUMDes serta adanya local hero, pembangunan infrastruktur penunjang atraksi dan aksesibilitas wisata, riset dan edukasi konservasi, serta integrasi skema pendanaan berkelanjutan blue finance dan branding ekowisata blue carbon. Dukungan lintas sektor, ekowisata mangrove Pangkal Babu berpotensi menjadi pusat inovasi wisata hijau dan berkontribusi nyata bagi pembangunan berkelanjutan di tingkat lokal maupun nasional.
FIRE SPOT IDENTIFICATION BASED ON HOTSPOT SEQUENTIAL PATTERN AND BURNED AREA CLASSIFICATION Sitanggang, Imas Sukaesih; Istiqomah, Nalar; Syaufina, Lailan
BIOTROPIA Vol. 25 No. 3 (2018): BIOTROPIA Vol. 25 No. 3 December 2018
Publisher : SEAMEO BIOTROP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11598/btb.2018.25.3.676

Abstract

Indonesia has the world's largest tropical peatlands of about 14.9 million hectares that have important life support roles. However, fire frequently occurs in peatlands. According to experts and field forest firefighters, fire hotspots that appear in a sequence of two to three days at the same location have a high potential of becoming a forest fire. This study aimed to determine the sequential patterns of hotspot occurrences, classify satellite image data and identify the fire spots. Fire spot identification was done using hotspot sequence patterns that were overlaid with burned area classification results. Sequential pattern mining using the Prefix Span algorithm was applied to identify sequences of hotspot occurrence. Maximum Likelihood method was applied to classify Landsat 7 satellite images toward identifying burned areas in Pulang Pisau and Palangkaraya in Central Kalimantan and Pontianak in West Kalimantan. Sequence patterns were overlaid with image classification results. The study results show that in Pulang Pisau, 26.19% of sequence patterns are located in burned areas and 72.62% sequence patterns were found in the buffer of burned area within a radius of one kilometer. As for Palangkaraya, there were 62.50% sequence patterns located in burned areas and 87.50% sequence patterns in the buffer of burned area within the radius of one kilometer. In total, there were 72.62% and 87.50% fire hotspots recorded in Pisau and Palangkaraya, respectively, which are strong indicators of peatland fires.
APPLICATION OF CMORPH DATA FOR FOREST/LAND FIRE RISK PREDICTION MODEL IN CENTRAL KALIMANTAN Indah Prasasti; Rizaldi Boer; Lailan Syaufina
International Journal of Remote Sensing and Earth Sciences Vol. 11 No. 1 (2014)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2014.v11.a2600

Abstract

Central Kalimantan Province is a region with high level of forest/land fire, especially during dry season. Forest/land fire is a dangerous ecosystem destroyer factor, so it needs to be anticipated and prevented as early as possible. CMORPH rainfall data have good potential to overcome the limitations of rainfall data observation. This research is aimed to obtain relationship model between burned acreage and several variables of rainfall condition, as well as to develop risk prediction model of fire occurrence and burned acreage by using rainfall data. This research utilizes information on burned acreage (Ha) and CMORPH rainfall data. The method applied in this research is statistical analysis (finding correlation and regression of two phases), while risk prediction model is generated from the resulting empirical model from relationship of rainfall variables using Monte Carlo simulation based on stochastic spreadsheet. The result of this study shows that precipitation accumulation for two months prior to fire occurrence (CH2Bl) has correlation with burned acreage, and can be estimated by using following formula (if rainfall ≤ 93 mm): Burnt Acreage (Ha) = 5.13 – 21.7 (CH2bl – 93) (R2 = 67.2%). Forest fire forecasts can be determined by using a precipitation accumulation for two months prior to fire occurrence and Monte Carlo simulation. Efforts to anticipate and address fire risk should be carried out as early as possible, i.e. two months in advance if the probability of fire risk had exceeded the value of 40%.
Karakteristik Vegetasi dan Tanah Hutan Pasca Kebakaran di Gunung Lawu, BKPH Lawu Selatan, KPH Lawu Ds : Vegetation and Soil Characteristics of Post-Fire Forest in Mount Lawu, BKPH Lawu Selatan, KPH Lawu Ds Irwansyah, Muh Yosrilrafiq; Istomo, Istomo; Syaufina, Lailan
JURNAL HUTAN TROPIKA Vol 20 No 2 (2025): Volume 20 Nomor 2 Tahun 2025
Publisher : Jurusan Kehutanan, Fakultas Pertanian Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36873/jht.v20i2.21564

Abstract

Forest fire constitutes a major ecological disturbance that induces substantial alterations in ecosystem structure and function, particularly affecting vegetation and soil properties. The Mount Lawu forest, situated within the management unit of BKPH Lawu Selatan, KPH Lawu Ds, has undergone multiple fire events over the past two decades. This study aims to examine characteristics across distinct successional stages, assess soil physicochemical attributes, and identify potential pioneer species capable of adapting to post-fire environments. The investigation was conducted across three successional stages represented by shrubland (burned in 2019), young secondary forest (burned in 2015), and old secondary forest (burned in 2002). Vegetation sampling employed nested plot design, while soil analysis included measurements of pH, organic carbon, total nitrogen, carbon-to-nitrogen ratio, cation exchange capacity, base saturation, and bulk density. The findings reveal successional shifts in species dominance from Imperata cylindrica and Schima wallichii in early stages to Lithocarpus sundaicus in mature forest stands. The assessment of physical and chemical soil properties showed variable results. Biomass accumulation increased along the successional gradient, indicating enhanced ecosystem development. Soil variables exhibited patterns in relation to vegetation structure and biomass.
Co-Authors Afina, Fakhri Sukma Agus Buono Agus Siswono Agus Siswono Ahmad Ainuddin Nuruddin Aisyah Anggraini Albar, Israr Andi Gunawan Andini Tribuana Tunggadewi, Andini Tribuana Anggie Yohanna Mandalahi Anissa Rezainy Anita Zaitunah Annisa Puspa Kirana Arzyana Sunkar Asri Buliyansih Atfi Indriany Putri Atfi Indriany Putri Ati Dwi Nurhayati Awal, Elsa Elvira Bahruni . Bambang Hero Saharjo Boedi Tjahjono Chandrasa E Sjamsudin Daniel Happy Putra Denni Prasetia Diah Zuhriana Didik Suharjito Dinda Aisyah Fadhillah Hafni Drucella Benala Dyahati Eduardo Fernando Martins de Carvalho Efendi, Zuliar Eka Intan Kumala Putri Eko Heriyanto Entin Kartini Erfan Noor Yulian Erianto Indra Putra Ernawati, Titik Firman Ardiansyah Fransisxo GS Tambunan Gatot Setiawan Gatot Setiawana Gusti Zainal Anshari Hariyadi Hendra Rahmawan Hendra Rahmawan Hidayat, Assad I Nengah Surati Jaya Iin Ichwandi Imam Suyodono Imas Sukaesih Sitanggang Indah Prasasti Indah Prasasti Irdika Mansur Irwansyah, Muh Yosrilrafiq Istiqomah, Nalar Istomo . Jamaluddin Basharuddin James Thomas Erbaugh Jumani Jumani Khaira, Ulfa Khairia Nafia Khulfi M Khalwani Komarsa Gandasasmita Krisnanto, Ferdian Kurniawati Purwaka Putri Lai Food See LILIK BUDIPRASETYO M. Syamsul Maarif M. Taufan Tirkaamiana M. Taufan Tirkaamiana Meti Ekayani Mirzha Hanifah Mochamad Asep Maksum Mohid Rashid Mohd Yusof Muhammad Ardiansyah Muhammad Hawari Azka Muhammad Hudzaifah Rihuljihad Muhammad Ikhsan Muhammad Imam Nugraha Muhammad Nur Aidi Nadhifah, Putri Addini Arsya Nining Puspaningsih Noor Farikhah Haneda Nova Puspitasari Nuniek Sutanti Nurheni Wijayanto Prima Trie Wijaya Purwanti , Endang Yuni Purwanti, Endang Yuni Putra, Aditya Handoyo Putri Thariqa Rinenggo Siwi Rizaldi Boer Rizki, Yoze Samsuri Samsuri, Samsuri Sandhi Imam Maulana Satyawan, Verda Emmelinda Sigit Purwanto Sitanggang, Imas S. Siti Badriyah Rushayati Sobri Effendy Sofia Fitriana Sri Mulatsih Sugiarto, Dwi Putro Supriyadi, Andi Supriyanto Supriyanto Suryawan Ramadhan Syaiful Anwar Taihuttu, Helda Yunita Tri Tiana Ahmadi Putri Trisminingsih, Rina Unik, Mitra Vera Linda Purba Wahida Annisa Wardana Wardana Widiatmaka Wiwin Ambarwulan WULANDARI Wulandari, Ratu Mutiara Yenni Vetrita Yuli Sunarti