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All Journal JURNAL ILMIAH GEOMATIKA Konvergensi International Journal of Remote Sensing and Earth Sciences (IJReSES) Pengmasku
Fajar Yulianto
Lembaga Penerbangan dan Antariksa Nasional
Humanities Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences

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KAPASITAS INDEKS LAHAN TERBAKAR NORMALIZED BURN RATIO (NBR) DAN NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) DALAM MENGIDENTIFIKASI BEKAS LAHAN TERBAKAR BERDASARKAN DATA SPOT-4 Parwati, Parwati; Zubaidah, Any; Vetrita, Yenni; Yulianto, Fajar; DS, Kusumaning Ayu; Khomarudin, M Rokhis
GEOMATIKA Vol 18, No 1 (2012)
Publisher : Badan Informasi Geospasial in Partnership with MAPIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1996.976 KB) | DOI: 10.24895/JIG.2012.18-1.193

Abstract

Pada penelitian ini, kapasitas indeks Difference Normalyzed Burn Ratio (dNBR) dan indeks Difference Normalized Vegetation Index (dNDVI)  sebagai indeks lahan terbakar telah dianalisis untuk mengidentifikasi lahan bekas terbakar di wilayah Provinsi Riau berdasarkan data SPOT-4. Baik dNBR maupun dNDVI merupakan selisih antara indeks NBR atau NDVI sebelum terjadi kebakaran (pre-fire) dengan sesudah terjadi kebakaran (post-fire). Data time-series SPOT-4 yang digunakan adalah periode Juli 2009, Oktober 2010, Maret 2011, Juni 2011 dan Juli 2011. Hasil analisis menunjukkan bahwa nilai ekstraksi NDVI atau NBR pada kondisi pre-fire mempunyai nilai yang lebih tinggi dibandingkan dengan lahan pada kondisi post-fire. Umumnya hal tersebut menunjukkan adanya perubahan dari tingkat kehijauan vegetasi yang tinggi menjadi rendah. Berdasarkan hasil verifikasi di lapangan (Agustus 2011), ternyata pada lahan bekas terbakar indeks dNBR (0.42) menunjukkan nilai yang lebih tinggi dibandingkan dengan dNDVI (0.19). Sementara di lokasi pembukaan lahan/hutan tanpa membakar, indeks dNDVI (0.53) lebih tinggi dibandingkan dNBR (0.05). Hal tersebut membuktikan bahwa indeks dNBR sangat sensitif dalam mengidentifikasi lahan bekas terbakar yang menghandalkan spektrum radiasi Shortwave Infrared (SWIR) yang peka terhadap rendahnya kadar air di lahan bekas terbakar. Sementara indeks dNDVI lebih cocok digunakan untuk mendeteksi perubahan lahan dari vegetasi ke non vegetasi tanpa membakar.Kata Kunci : SPOT-4, lahan bekas terbakar, dNBR, dNDVI, Riau ABSTRACTIn this study, Difference Normalyzed Burn Ratio (dNBR) and Difference Normalized Vegetation Index (dNDVI) derived from SPOT-4 images were analyzed for identifying burn scar in Riau Province.The dNBR and dNDVI are the differences between NBR or NDVI in pre-fire condition and in post-fire condition. The time-series SPOT-4 images used in this study  have accusition month onJuly2009, October 2010, March 2011, June 2011, and July 2011. Results show that both NDVI and NBR have higher values in pre-fire rather than in post-fire condition. Generally, it shows the change in green vegetation level from high in vegetation cover to lower level in burnt area. However, by referring to field survey data (August 2011), the dNBR (0.42) shows higher value than the dNDVI (0.19) in burnt area. The indices were also applied in opened land/forest without burning activity which showed higher dNDVI (0.53) values rather than dNBR (0.05). Therefore, it has been proved that the dNBR index is more suitable to identify burnt area which has Shortwave Infrared (SWIR) spectrum that is more sensitive to moisture content in burnt area. Meanwhile the dNDVI could be used to identify forest changes to non forest cover without burning activitiy.Key Words : SPOT-4, burn scar, dNBR, dNDVI, Riau
ANALISA SENTIMEN UNTUK MENGIDENTIFIKASI KECENDERUNGAN RADIKALISME DENGAN NAÏVE BAYES Yulianto, Fajar; Junaedi, Hartarto; Tjandra, Suhatati; Pascarini, Amanda
KONVERGENSI Vol 17 No 2 (2021)
Publisher : Informatics, Universitas 17 Agustus 1945 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30996/konv.v17i2.5470

Abstract

ABSTRACTRadicalism in Indonesia is still an issue that is often discussed considering that there are still many acts of radicalism in Indonesia. The relatively rapid spread of radicalism requires special handling to stop the spread. This study aims to identify radicalism by grouping it into 3 (three) groups or levels which are later expected to facilitate the search for solutions to stop the spread of radicalism. The analysis system will use sentiment analysis using the method of machine learning, namely Naive Bayes. The data used were collected through surveys to students and religious leaders. The Naive Bayes method will later serve to determine the survey results of each individual based on his group. The total data collected for this study amounted to 250 respondents who have filled out survey questions, the data will be divided into 2 types, there are 165 data used for the training phase and 85 data for testing. After processing the data, the results were obtained using classification report calculations and obtained an accuracy of 85% from 3 radical groups. Keywords: sentiment analysis; machine learning; naïve Bayes; radicalism ABSTRAKRadikalisme di Indonesia, hingga saat ini masih menjadi suatu isu yang diperbincangkan mengingat masih banyaknya aksi-aksi radikalisme di Indonesia. Penyebaran paham radikalisme yang relative cepat memerlukan adanya penanganan khusus untuk menghentikan penyebaran tersebut. Penelitian ini bertujuan untuk mengidentifikasi radikalisme dengan mengelompokannya menjadi 3 (tiga) kelompok atau tingkatan yang nantinya diharapkan dapat memudahkan pencarian solusi penghentian penyebaran paham radikalisme tersebut. Sistem analisis akan menggunakan analisis sentiment dengan menggunakan metode dari machine learning yaitu naive bayes. Data yang digunakan sendiri dikumpulkan melalui survey kepada santri dan pemuka agama. Metode naive bayes nantinya akan berfungsi untuk menentukan hasil survey tiap individu tersebut berdasarkan kelompoknya. Total data yang terkumpul untuk penelitian ini berjumlah 250 responden yang sudah mengisi pertanyaan survey, data tersebut nantinya akan dibagi 2 jenis, terdapat 165 data digunakan untuk tahap training dan 85 data untuk testing. Setelah melakukan proses olah data hasil yang didapat menggunakan perhitungan classification report dan didapat akurasi sebesar 85% dari 3 kelompok radikal. Kata Kunci: sentiment analysis; machine learning; naïve bayes; radikalisme
DETECTING THE SURFACE WATER AREA IN CIRATA DAM UPSTREAM CITARUM USING A WATER INDEX FROM SENTINEL-2 Suwarsono, Suwarsono; Yulianto, Fajar; Fitriana, Hana Listi; Nugroho, Udhi Catur; Sukowati, Kusumaning Ayu Dyah; Khomarudin, Muhammad Rokhis
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 17, No 1 (2020)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2020.v17.a3286

Abstract

This paper describes the detection of the surface water area in Cirata dam,  upstream Citarum, using a water index derived from Sentinel-2. MSI Level 1C (MSIL1C) data from 16 November 2018 were extracted into a water index such as the NDWI (Normalized Difference Water Index) model of Gao (1996), McFeeters (1996), Roger and Kearney (2004), and Xu (2006). Water index were analyzed based on the presence of several objects (water, vegetation, soil, and built-up). The research resulted in the ability of each water index to separate water and non-water objects. The results conclude that the NDWI of McFeeters (1996) derived from Sentinel-2 MSI showed the best results in detecting the surface water area of the reservoir.
Edukasi Dan Pendampingan Untuk Penyampaian SPT Pajak Penghasilan Bagi Wajib Pajak Pada Kantor Pelayanan Pajak Pratama Pondok Aren Marfiana, Andri; Haniyah, Rizqi; Yulianto, Fajar
Pengmasku Vol 4 No 1 (2024)
Publisher : PT WIM Solusi Prima

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54957/pengmasku.v4i1.1123

Abstract

This community service program aims to enhance tax compliance through education and assistance in the filing of Annual Tax Returns (SPT) for individual taxpayers (WP OP), micro, small, and medium enterprises (UMKM), and corporate taxpayers. The program involves tax volunteers from PKN STAN in collaboration with KPP Pratama Pondok Aren. The educational activities focus on guiding taxpayers in filing SPT independently, preparing financial statements, and understanding their tax obligations. Additionally, tax-related infographics were disseminated via social media to reach a wider audience. The assistance was provided directly at KPP Pratama Pondok Aren and PKN STAN building. The results indicate an improvement in taxpayers' understanding of how to file SPT and overcome technical issues such as forgotten EFINs and server problems. This initiative made a positive contribution to enhancing taxpayer compliance, with the potential for broader implementation in the future. Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan kepatuhan perpajakan melalui edukasi dan pendampingan pengisian Surat Pemberitahuan (SPT) Tahunan bagi wajib pajak orang pribadi (WP OP), usaha mikro, kecil, dan menengah (UMKM), serta wajib pajak badan. Program ini melibatkan relawan pajak dari PKN STAN yang bekerja sama dengan KPP Pratama Pondok Aren. Kegiatan edukasi meliputi pengisian SPT secara mandiri, penyusunan laporan keuangan, dan pemahaman terhadap kewajiban perpajakan. Selain itu, infografis perpajakan disebarkan melalui media sosial untuk menjangkau masyarakat lebih luas. Pendampingan dilakukan secara langsung di KPP Pratama Pondok Aren dan Gedung PKN STAN. Hasil kegiatan menunjukkan peningkatan pemahaman wajib pajak dalam mengisi SPT dan mengatasi kendala teknis seperti lupa EFIN dan masalah server. Kegiatan ini berkontribusi positif dalam meningkatkan kepatuhan wajib pajak, dengan potensi untuk diperluas cakupannya di masa mendatang.
Co-Authors Any Zubaidah Hana Listi Fitriana, Hana Listi Haniyah, Rizqi Hartarto Junaedi Khomarudin, Muhammad Rokhis Kusumaning Ayu DS, Kusumaning Ayu M Rokhis Khomarudin, M Rokhis Marfiana, Andri Parwati, Parwati Pascarini, Amanda Suhatati Tjandra Sukowati, Kusumaning Ayu Dyah Suwarsono Suwarsono Udhi Catur Nugroho, Udhi Catur Yenni Vetrita, Yenni