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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmu dan Teknologi Kelautan Tropis IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Informatika Jurnal Simetris Elkom: Jurnal Elektronika dan Komputer Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JTSL (Jurnal Tanah dan Sumberdaya Lahan) Jurnal Transformatika Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Faktor Exacta Jurnal Ilmiah Matrik JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Indonesian Journal of Computing and Modeling J-SAKTI (Jurnal Sains Komputer dan Informatika) JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Journal Sensi: Strategic of Education in Information System JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA Aiti: Jurnal Teknologi Informasi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Journal of Information Technology (JIfoTech) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Info Sains : Informatika dan Sains Jurnal Nasional Teknik Elektro dan Teknologi Informasi IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Jurnal Informatika: Jurnal Pengembangan IT Jurnal Indonesia : Manajemen Informatika dan Komunikasi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Sri Yulianto Joko Prasetyo
Universitas Kristen Satya Wacana
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Sebaran Vegetasi pada Kawasan Berpotensi Bencana Banjir Pesisir (Rob) Kota Semarang Daniel Kurniawan; Sri Yulianto Joko Prasetyo; Charitas Fibriani
Indonesian Journal of Computing and Modeling Vol 4 No 1 (2021)
Publisher : Pusat Studi Sistem Informasi dan Pemodelan Mitigasi Tropika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/icm.v4i1.4653

Abstract

Abstrak - Banjir pesisir atau rob adalah banjir yang terjadi karena masuknya air laut ke area daratan dan menjadi masalah utama pada daerah di Indonesia yang besinggungan langsung dengan daerah pantai seperti Kota Semarang. Angin, tinggi gelombang, curah hujan, dan topologi tanah menjadi faktor yang sangat mempengaruhi perbedaan tinggi permukaan air laut dari muka daratan. Mangrove merupakan salah satu vegetasi yang cocok untuk daerah pantai karena dapat tumbuh di lingkungan dengan kadar garam yang tinggi. Keadaan permukaan Kota Semarang seperti persebaran vegetasi dan ketinggian tanah dapat diketahui dengan memanfaatkan teknologi remote sensing atau penginderaan jauh. Indeks vegetasi seperti NDVI (Normalized Different Vegetation Index) menjadi salah satu metode yang dipakai untuk menganalisa persebaran dan tingkat kehijauan pada suatu wilayah. Selain NDVI dalam penelitian juga memanfaatkan peta DEM (Digital Elevtion Model) untuk mengetahui kondisi ketinggian atau kontur dalam wilayah tertentu. Tingkat kehijauan di daerah utara Kota Semarang lebih banyak memiliki nilai kehijauan rendah hingga sangat rendah dan 9 dari 16 Kecamatan di Kota Seamarang memiliki kontur ketinggian tanah kurang dari 25 meter. Dari kedua keadaan tersebut bencana banjir pesisir atau rob akan sulit untuk dihindari dan perlu adanya peranan dari masyarakat maupun pemerintah untuk menangani masalah banjir pesisir di Kota Semarang.
Model Penilaian Tata Guna Lahan Dengan Citra Landsat 8 OLI Menggunakan Algoritma XGBoost Diwilayah Beresiko Tsunami (Studi Kasus : Kota Palu Sulawesi Tengah) Yulia Fransisca Wijaya; Sri Yulianto Joko Prasetyo
Indonesian Journal of Computing and Modeling Vol 4 No 1 (2021)
Publisher : Pusat Studi Sistem Informasi dan Pemodelan Mitigasi Tropika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/icm.v4i1.4981

Abstract

Tsunami merupakan salah satu bencana alam yang berbahaya dimana dapat memakan korban jiwa, gelombang air yang besar pada bencana tsunami dapat bergerak sangat cepat dan dapat menghancurkan wilayah pemukiman yang berada dekat dengan laut. Dimana Indonesia merupakan salah satu negara yang berada diurutan pertama dari 256 negara didunia dengan ancaman tsunami yang tinggi. Pada tanggal 28 September 2018 terjadi sebuah tsunami pada Kota Palu yang memakan korban jiwa sebanyak 3.689 orang hilang dan meninggal. Penelitian ini bertujuan untuk membuat sebuah model peta yang dapat memberikan informasi mengenai klasifikasi lahan beresiko tsunami. Tingkat klasifikasi lahan beresiko tsunami dibagi menjadi 5 klasifikasi yaitu sangat rawan, tinggi, rendah, sangat rendah, dan tidak rawan. Hasil penelitian yang didapatkan pada nilai akurasi untuk semua parameter sebesar 0.909, sedangkan nilai perulangan pertama train mlogloss sebesar 0.6926 dan test mlogloss 0.6928, dan untuk perulangan keseratus mendapatkan nilai train mlogloss 0.6437 dan test mlogloss 0.6547, diketahui bahwa semakin banyak melakukan perulangan nilai dari pada test mglogloss dan train mglogloss akan semakin kecil perluang dari kesalahan Extreme Gradient Boosting. Berdasarkan hasil klasifikasi antara data yang sudah dan belum diprediksi menggunakan Extreme Gradient Boosting didapatkan 43 kelurahan yang mempunyai hasil yang berbeda. Sehingga Extreme Gradient Boosting dapat digunakan untuk pengambilan keputusan dalam membuat model klasifikasi lahan beresiko tsunami.
Model Penilaian Tata Guna Lahan Dengan Citra Landsat 8 OLI Menggunakan Algoritma XGBoost Diwilayah Beresiko Tsunami (Studi Kasus : Kota Palu Sulawesi Tengah) Yulia Fransisca Wijaya; Sri Yulianto Joko Prasetyo
Indonesian Journal of Computing and Modeling Vol 4 No 1 (2021)
Publisher : Pusat Studi Sistem Informasi dan Pemodelan Mitigasi Tropika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/icm.v4i1.4981

Abstract

Tsunami merupakan salah satu bencana alam yang berbahaya dimana dapat memakan korban jiwa, gelombang air yang besar pada bencana tsunami dapat bergerak sangat cepat dan dapat menghancurkan wilayah pemukiman yang berada dekat dengan laut. Dimana Indonesia merupakan salah satu negara yang berada diurutan pertama dari 256 negara didunia dengan ancaman tsunami yang tinggi. Pada tanggal 28 September 2018 terjadi sebuah tsunami pada Kota Palu yang memakan korban jiwa sebanyak 3.689 orang hilang dan meninggal. Penelitian ini bertujuan untuk membuat sebuah model peta yang dapat memberikan informasi mengenai klasifikasi lahan beresiko tsunami. Tingkat klasifikasi lahan beresiko tsunami dibagi menjadi 5 klasifikasi yaitu sangat rawan, tinggi, rendah, sangat rendah, dan tidak rawan. Hasil penelitian yang didapatkan pada nilai akurasi untuk semua parameter sebesar 0.909, sedangkan nilai perulangan pertama train mlogloss sebesar 0.6926 dan test mlogloss 0.6928, dan untuk perulangan keseratus mendapatkan nilai train mlogloss 0.6437 dan test mlogloss 0.6547, diketahui bahwa semakin banyak melakukan perulangan nilai dari pada test mglogloss dan train mglogloss akan semakin kecil perluang dari kesalahan Extreme Gradient Boosting. Berdasarkan hasil klasifikasi antara data yang sudah dan belum diprediksi menggunakan Extreme Gradient Boosting didapatkan 43 kelurahan yang mempunyai hasil yang berbeda. Sehingga Extreme Gradient Boosting dapat digunakan untuk pengambilan keputusan dalam membuat model klasifikasi lahan beresiko tsunami.
Perancangan Arsitektur Sistem Aplikasi DOOR to DOOR Kantor UPPD SAMSAT Kota Salatiga Berbasis Android Firda Rachman; Sri Yulianto Joko Prasetyo
Journal of Information System Research (JOSH) Vol 2 No 4 (2021): Juli 2021
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (450.27 KB) | DOI: 10.47065/josh.v2i4.798

Abstract

The increase in the number of motor vehicles is difficult to anticipate, the roads used are not in accordance with capacity. To prevent congestion, the government imposes a progressive tax on motor vehicles that have moved owners but are still on behalf of the previous owner. Manunggal Administration System under One Roof (SAMSAT) conducts Door To Door activities to request direct confirmation to taxpayers whose vehicles are subject to progressive taxes. It is difficult for taxpayers to find out what makes data manipulation happen. Created Android-based Door to Door Application System using waterfall method to facilitate Door to Door activities and avoid manipulation of taxpayer data. By using waterfall method this application is made starting from the analysis of needs, design of system design, implementation, to the application of the program. Application system design is built using UML (Uniferd Modeling Language) system design and system design implementation using Android Studio and RestAPI.
Perancangan Arsitektur Sistem Aplikasi DOOR to DOOR Kantor UPPD SAMSAT Kota Salatiga Berbasis Android Firda Rachman; Sri Yulianto Joko Prasetyo
Journal of Information System Research (JOSH) Vol 2 No 4 (2021): Juli 2021
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (450.27 KB) | DOI: 10.47065/josh.v2i4.798

Abstract

The increase in the number of motor vehicles is difficult to anticipate, the roads used are not in accordance with capacity. To prevent congestion, the government imposes a progressive tax on motor vehicles that have moved owners but are still on behalf of the previous owner. Manunggal Administration System under One Roof (SAMSAT) conducts Door To Door activities to request direct confirmation to taxpayers whose vehicles are subject to progressive taxes. It is difficult for taxpayers to find out what makes data manipulation happen. Created Android-based Door to Door Application System using waterfall method to facilitate Door to Door activities and avoid manipulation of taxpayer data. By using waterfall method this application is made starting from the analysis of needs, design of system design, implementation, to the application of the program. Application system design is built using UML (Uniferd Modeling Language) system design and system design implementation using Android Studio and RestAPI.
Evaluasi Kinerja Pembelajaran Learning Management System Menggunakan COBIT 4.1 pada Universitas STEKOM Semarang Haikal Nur Rachmanrachim Achaqie; Eko Sediyono; Sri Yulianto Joko Prasetyo
Elkom : Jurnal Elektronika dan Komputer Vol 15 No 1 (2022): Juli : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v15i1.788

Abstract

Universitas Ilmu Komputer dan Teknologi (STEKOM University) telah menggunakan Learning Management System (LMS) sejak tahun 2018, namun hingga saat ini pengukuran kinerja Learning Management System (LMS) belum dilakukan. Penelitian ini bertujuan untuk mengukur tingkat kematangan Learning Management System (LMS) dengan menggunakan Framework COBIT 4.1 pada domain Delivery and Support (DS) dan Monitoring and Evaluation (ME). Nilai tingkat kematangan pada kondisi eksisting berada pada level rata-rata 2, sedangkan kondisi yang ingin dicapai berada pada level rata-rata 3. Untuk mencapai level yang diharapkan, saran perbaikan mengacu pada Kerangka COBIT 4.1 perlu yang akan diberikan antara lain: pembuatan SOP (Standar Operasional dan Prosedur) LMS, kelengkapan isi LMS, tertib administrasi dokumentasi arsip penting, realisasi pelatihan LMS bagi dosen, alokasi biaya pemeliharaan dan pengujian sistem, penerapan reward and punishment, pembuatan dan penggunaan framework dan E governance - Pembelajaran dan semuanya dilakukan secara rutin minimal setiap 6 bulan sekali.
Comparison of IDW and Kriging Interpolation Methods Using Geoelectric Data to Determine the Depth of the Aquifer in Semarang, Indonesia Brilliananta Radix Dewana; Sri Yulianto Joko Prasetyo; Kristoko Dwi Hartomo
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 2 (2022): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i2.23260

Abstract

Several areas in Semarang City have been unable to get a clean water supply through the Local Water Company (PDAM) channel. One of the solutions that can be done to overcome this problem is by utilizing groundwater, which can be obtained by building a deep well made to obtain rock layers that can accommodate and drain groundwater (aquifer layer). To find out the approximate depth of the aquifer layer, it is necessary to conduct a preliminary investigation before drilling. There are so many methods that can be done, and one of them is by using the geoelectric method. After using the geoelectric method, we can determine the distribution of the depth of the aquifer in Semarang City by using interpolation analysis. In this study, the IDW and Kriging interpolation methods were used. The two methods were then compared to show the difference in the distribution of aquifer depths in areas that lack clean water using the two interpolation methods above. Besides that, we are using RMSE and MAPE analysis to find the error rate of the two methods. The results obtained were the RMSE of the IDW and Kriging methods amounting to 5,829 and 5,433, and the MAPE results were 10.90% and 10.34%. Based on this, the Kriging method tends to have better results when interpolating using geoelectric data. With this research, it is hoped to provide knowledge to determine the most suitable interpolation method used in determining the depth of the aquifer and also can be used as an illustration of the depth of the aquifer in the area that lacked clean water in Semarang City, so that it can be used as a reference in estimating the design of deep good development more accurately.
Penilaian Mutu Pendidikan Berdasarkan Ketersediaan Sarana Prasarana di Masa Pandemi Covid-19 Menggunakan Metode AHP Gilbert Yesaya Likumahua; Sri Yulianto Joko Prasetyo
JURIKOM (Jurnal Riset Komputer) Vol 9, No 5 (2022): Oktober 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i5.4937

Abstract

This study aims to know the infrastructures used for the teaching-learning process and the effort made to improve education quality during the pandemic of Covid-19. The method used by the researcher was descriptive qualitative. The subjects of this study were four senior high schools in Salatiga. AHP method approach was used in conducting this study. Meanwhile, Questionnaires and interviews were used as instruments for data retrieval. The data collected will be analyzed using literature-based data triangulation techniques. The results showed that some criteria have a high impact on improving the quality of education based on infrastructure tools: bandwidth with 0,397 (39%), lcd with 0,245 (24%), computer lab with 0,242 (24%), the  number of computers 0,064 (6%), and the last was the access point with 0.053 (5%).
TWITTER SENTIMENT ANALYSIS PEDULILINDUNGI APPLICATION USING NAÏVE BAYES AND SUPPORT VECTOR MACHINE Indra Yunanto; Sri Yulianto
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 4 (2022): JUTIF Volume 3, Number 4, August 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.4.292

Abstract

The PeduliLindungi application is an application launched by the government during the COVID-19 pandemic, with the aim of helping government agencies carry out digital tracking to monitor the public, as an effort to prevent the spread of the Corona virus. Many people express their opinions on the PeduliLindung application on social media, one of which is through Twitter. To improve the performance of the application, of course, need input or complaints from users, opinions from the public on Twitter about the PeduliLindungi application can be input to improve or improve the performance of the application. Sentiment analysis is carried out to see how the public's sentiment towards the PeduliLindung application is, and these sentiments will be categorized into positive sentiment and negative sentiment, this sentiment can later be used as evaluation material for application development. This study aims to see and compare the accuracy of two classification methods, Naïve Bayes and Support Vector Machine in the classification process of sentiment analysis. The data used are 4636 tweets with the keyword " PeduliLindungi". The data obtained then goes to the pre-processing stage before going to the classification stage. The results obtained after classifying using the Naïve Bayes method and the Support Vector Machine show that the Support Vector Machine method has a higher accuracy of 91%, while the Naïve Bayes method has an accuracy of 90%.
IDENTIFICATION OF THE COVID-19 DISTRIBUTION AREA ON THE ISLAND OF KALIMANTAN USING THE K-MEANS SPATIAL CLUSTERING METHOD Fabian Valerian; Sri Yulianto
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 4 (2022): JUTIF Volume 3, Number 4, August 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.4.314

Abstract

Based on the map of the spread of COVID-19 in Indonesia, Kalimantan Island is the second island with the number of COVID-19 distributions after Java Island. The purpose of this study is to provide information to the entire community and government, especially the Kalimantan region regarding the clustering of the spread of COVID-19. The K-Means algorithm method used in the grouping is based on data on positive, recovered, and deceased people collected by each province on the island of Kalimantan, then a geographic information system (GIS) is applied in mapping to display the clustered distribution area of ​​each district on the island of Kalimantan. The result of this research is that the k-means algorithm is able to classify data with low, medium, and high distribution levels so that later the distribution area can be mapped using GIS based on the results of the clustering. With the results of this application, it is hoped that it can be used as information for the government and also the public to think about what efforts should be made if bad things happen later, based on the level of spread to be used as a priority scale in controlling the spread of the COVID-19 virus.
Co-Authors Adenia Kusuma Dayanthi Anna Simatauw Antar Maramba Jawa Antonius Mbay Ndapamury Ardian Ariadi Ardito Laksono Suryoputro Arit Imanuel Meha Arvira Yuniar Isnaeni Ayuningtyas, Fajar Baali, Gabriel Megfaden Kenisa Baronio, Nodas Constantine Bintang Lazuardi Bistok Hasiholan Simanjuntak Brian Laurensz Brilliananta Radix Dewana Bunga, Alex Frianco Cahyaningtyas, Christian Charitas Fibriani Christanto, Erwien Christiana Ari Setyaningrum Daniel HF Manongga Danny Manongga Danny Sebastian Devianto, Yudo Dian Widiyanto Chandra Dwi Hayati Edwin Zusrony Eko Sediyono Elvira Umar Engles Marabangkit Yoesmarlan Erik Wahyu Abdi Nugroho Evan Bagus Kristianto Evan Geraldy Suryoto Evi Maria Fabian Valerian Feibe Lawalata Florentina Tatrin Kurniati Gallen cakra adhi wibowo Gideon Bartolomeus Kaligis Gilbert Yesaya Likumahua Gudiato, Candra Haikal Nur Rachmanrachim Achaqie Haikal Nur Rachmanrachim Achaqie Hindriyanto Dwi Purnomo Ida Ayu Putu Sri Widnyani Indra Yunanto Irdha Yunianto Irwan Sembiring Isnaeni, Arvira Yuniar Josua Josen Alexander Limbong Kase, Celomitha Putri Welhelmina Kristia Yuliawan Kristoko Dwi Hartomo Kurnia Latifatul Nazila Laurentius Kuncoro Probo Saputra Lobo, Murry Albert Agustin Lyonly Evany Tomasoa Maipauw, Musa Marsel Maya Sari Merryana Lestari Mikhael Dio Eclesi Mila Chrismawati Paseleng Mira Mira Muhamad Yusup Muhammad Rizky Pribadi Muhammad Sholikhan Nadia Renatha Yuwono Nadya Inarossy Novem Berlian Uly Nugroho, Ignatius Dion Nusantara, Bandhu Otniel, Marcelinus Vito Patrick Simbolon Permatasari, Aurilia Dinda Petty, Holbed Joshua Praditya, Al-Farrel Raka Prayitno, Gunawan Priyadi Priyadi Purwoko, Agus Qurotul Aini Ratu, Herman Huki Ravensca Matatula Raymond Elias Mauboy Riko Yudistira Rina Pratiwi Pudja I. A Rohmad Abidin, Rohmad Rony, Zahara Tussoleha Roy Rudolf Huizen Santoso, Nuke Puji Lestari Septian Silvianugroho Septio, Pius Aldi Solly Aryza Sri Hartati Stanny Dewanty Rehatta Stevanus Dwi Istiavan Mau Supit, Christanti Ekkelsia Suryasatria Trihadaru Suryasatriya Trihandaru Susatyo, Yeremia Alfa Sutarto Wijono Theopillus J. H. Wellem Tirsa Ninia Lina Triloka Mahesti Triloka Mahesti Untung Rahardja Valentino Kevin Sitanayah Que Vinsensius Aprila Kore Dima Wahani, Puteri Justia Kardia Momuat Wasis Pancoro Wicaksono, Muhammad Ryqo Jallu Winarko, Edi Wiwin Sulistyo Yansen Bagas Christianto Yerik Afrianto Singgalen Yesi Arumsari Yohanes Aji Priambodo