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Kusrini, Kusrini

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POTENTIAL ENTRY OF DHF DISEASE BASED ON ENVIRONMENTAL CONDITIONS USING ARTIFICIAL METHODS NEURAL NETWORK PERCEPTION S, Muhammad Sabri; Herlinawati, Noor; MZ, Reza Rafiq; Kusrini, Kusrini
Device Vol 14 No 2 (2024): November
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/device.v14i2.7694

Dengue Hemorrhagic Fever (DHF) is an infectious disease caused by the dengue virus transmitted by the Aedes aegypti mosquito. The spread of DHF is greatly influenced by environmental conditions such as temperature, rainfall, humidity, and population density. In Indonesia, DHF has become a significant public health problem, especially in densely populated urban areas. Therefore, it is important to develop a predictive model that can forecast the potential occurrence of DHF based on environmental variables to reduce the impact and control the spread of this disease. The objective of this research is to develop a predictive model using the Artificial Neural Network Perception (ANN) method to predict the potential occurrence of DHF based on environmental variables, and to create an application for predicting the potential of DHF. This model is expected to help authorities make appropriate decisions to prevent and control DHF outbreaks. The research methodology includes the following stages: data collection, data preprocessing, ANN model development, model evaluation, and implementation and validation. The expected output of this research is an ANN model that can accurately predict the potential occurrence of DHF based on environmental conditions. Additionally, it is hoped that a predictive system will be available for authorities to take effective preventive and control measures against DHF. The research is expected to make a significant contribution to public health, particularly in the prevention and control of DHF. The results include an application for predicting the potential occurrence of DHF in a specific area, with features such as a Dashboard Interface, Temperature Interface, Dataset Interface, and Result Model Interface. The RMSE results obtained for this research were 0.01441372. From the research results, it can be concluded that ANN can be used to predict the potential for dengue fever to enter.

Fotografi Konseptual sebagai Media Representasi Sikap Masyarakat Lokal Terhadap Fenomena "Udan Salah Mangsa" Kusrini, Kusrini; Susanto Anom Purnomo, Aji
Rekam Vol 19, No 2 (2023): Oktober 2023
Publisher : Institut Seni Indonesia Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24821/rekam.v19i2.9355

Artikel ini memuat hasil penelitian tentang fotografi konseptual yang digunakan sebagai media representasi sikap masyarakat lokal terhadap fenomena hujan di musim yang salah (udan salah mangsa). Penelitian menggunakan pendekatan fotografis untuk memahami fenomena udan salah mangsa dalam perspektif masyarakat lokal. Bagaimana masyarakat merespon anomali hujan dan peristiwa alam yang ada di lingkungannya dalam kaitan dengan isu perubahan iklim. Pendekatan penelitian yang digunakan adalah kualitatif dengan metode penyelesaian masalah penelitian dilakukan melalui proses berpikir kreatif dengan landasan pemikiran tentang seni konsep, fotografi konseptual, serta representasi. Adapun hasil penelitian menunjukkan bahwa fotografi konseptual udan salah mangsa merupakan media yang tepat untuk merepresentasikan sikap masyarakat lokal terhadap fenomena anomali hujan. Bangunan utama dalam penciptaan fotografi konseptual yaitu konsep dan ketrampilan fotografi, berhasil diolah secara kuat dan sebagian telah diwujudkan dalam purwarupa karya visual foto konsep. Konsep yang kuat dapat diperoleh melalui pengumpulan data dari berbagai sumber agar akurat serta pemahaman tentang fotografi yang baik sehingga karya diharapkan memiliki daya ganggu kognitif sehingga muncul kesadaran terhadap kondisi lingkungan alam. Conceptual Photography as Representation Media of Local Communities in Responding The Rains in The Wrong Season Phenomena. This article contains the results of research on conceptual photography which is used as a media to represent local people's attitudes towards the phenomenon of rain in the wrong season (udan salah mangsa). This research uses a photographic approach to understand the phenomenon of udan salah mangsa from the local community perspective. How do people respond to rain anomalies and natural events in their environment, including in relation to the issue of climate change. The research approach used is qualitative with the method of solving research problems carried out through a process of creative thinking with the basis of ideas about concept art, conceptual photography, and representation. The results of the study show that conceptual photography of udan salah mangsa is an appropriate medium to represent local people's attitudes towards the rain anomaly phenomenon. The main building blocks in the creation of conceptual photography, namely the concept and skills of photography, have been successfully processed and some of them have been embodied in prototypes of visual concept photo works. A strong concept can be obtained through collecting data from various sources so that it is accurate as well as a good understanding of photography so that works are expected to have cognitive interference so that awareness of natural environmental conditions arises.

PEMBUATAN TERASI IKAN LAYANG (Decapterus) MELALUI METODE FERMENTASI PADA MASYARAKAT LOWU-LOWU Kusrini, Kusrini; Iksan, Muhamad; Santri, Santri; Zumarni, Zumarni
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 5 No. 1 (2024): Volume 5 No 1 Tahun 2024
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v5i1.25315

Ikan merupakan makanan pokok manusia yaitu sebagai sumber protein bagi tubuh. Oleh karena banyaknya kandungan gizi dalam ikan, maka ikan dijadikan sebagai salah satu makanan pokok oleh manusia. Sehingga petani ikan gemar berburu ikan diantaranya masyarakat Lowu-lowu sebagai kebutuhan sehari-hari, dijual dan diolah sealah kadarnya, namun belum medapat menangani kelimpahan hasil tangkap ikan tersebut. Dengan demikian perlu inovasi baru yaitu pembuatan terasi ikan yaitu ikan Layang (Decapterus). Tujuan kegiatan ini adalah untuk menginovasi petani ikan Lowu-lowu untuk membuat terasi sebagai salah satu upaya penanganan penangkapan ikan yang berlimpah. Metode kegiatan ini adalah ceramah dan praktek. Kegiatan ceramah, yaitu menyampaikan materi terkait ruang lingkup ikan dan metode fermentasi pembuatan terasi ikan dan praktek pembuatan terasi oleh peserta kegiatan. Hasil pelaksanaan kegiatan ini adalah petani ikan kelurahan Lowu-lowu dapat memahami dan membuat langsung terasi ikan layang (Decapterus) melalui metode fermentasi. Sebagai inovasi dalam pengolahan hasil tangkap ikan yang melimpah.

EMPOWERING RURAL EDUCATORS THROUGH AI LITERACY: CHATGPT TRAINING AT SD NEGERI 3 SIBETAN KARANGASEM BALI Hamdikatama, Bimantyoso; Kusrini, Kusrini; Utami, Ema
Mitra Mahajana: Jurnal Pengabdian Masyarakat Vol. 6 No. 2 (2025): Volume 6 Nomor 2 Juli 2025
Publisher : LPPM Universitas Flores

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37478/mahajana.v6i2.5853

The advancement of Artificial Intelligence (AI) has had a significant impact on various sectors, including education. However, the adoption of AI in Indonesia remains uneven, particularly in remote and rural areas. This study aims to assess the effectiveness of a training program on the use of ChatGPT as a teaching aid for elementary school teachers at SD Negeri 3 Sibetan, Karangasem, Bali. The training was designed to enhance teachers' understanding, practical skills, and perceptions of AI integration in education. Using a quantitative approach with a one-group pretest-posttest experimental design, data were collected through conceptual knowledge tests, practical skill observations, and perception questionnaires. The results revealed a significant increase in teachers' knowledge, with average posttest scores rising from 32.2 to 78.0. Additionally, practical skills improved notably, as indicated by a posttest average score of 73.0. Positive perception also increased, with 71% of participants expressing enthusiasm for using ChatGPT in the classroom. Despite limited infrastructure, the training successfully introduced AI-based tools to rural educators, demonstrating the transformative potential of AI in promoting equitable, innovative, and interactive education. This study contributes to the discourse on AI in education and underscores the importance of contextualised teacher training in rural settings.

Prediksi Nilai Dan Waktu Kelulusan Mahasiswa Menggunakan Metode Svm (Studi Kasus: Universitas KH A Wahab Hasbullah Jombang Abdullah, Mochamad Fadillah; Kusrini, Kusrini; Arief, M. Rudyanto
SAINTEKBU Vol. 14 No. 01 (2022): Vol. 14 No. 01 January 2022
Publisher : KH. A. Wahab Hasbullah University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32764/saintekbu.v14i01.1096

Kelulusan mahasiswa merupakan salah satu yang harus diperhatikan karena masuk dalam Standar Penjaminan Mutu Internal suatu perguruan tinggi . Fakultas Teknologi Informasi merupakan salah satu fakultas yang di universitas KH A Wahab Hasbullah Jombang. Untuk kelulusan terdapat standar yang akan dicapai oleh fakultas tersebut yaitu waktu studi selama 4 tahun dan IPK minimal 3,00. Untuk dapat mencapai mutu kelulusan tersebut dibutuhkan suatu prediksi tingkat kelulusan dengan standar yang telah ditetapkan untuk mahasiswa yang masih menjalankan studi sehingga dapat dilakukan antisipasi dari awal sehingga dapat menanggulangi terjadinya permasalahan dalam bidang akademik. Untuk memprediksi tingkat kelulusan dan IPK standar tersebut digunakan metode data mining dengan fungsi klasifikasi. Metode klasifikasi yang digunakan menggunakan metode SVM. Perangkat yang digunakan untuk mengolah data yaitu software Rapid Miner.

Prediksi Tingkat Keberhasilan Studi Kinerja Santri Menggunakan Algoritma C 5.0 Miftachuddin, Achmad Agus Athok; Kusrini, Kusrini; Luthfi, Emha Taufiq
SAINTEKBU Vol. 13 No. 01 (2021): Vol. 13 No. 01 Januari 2021
Publisher : KH. A. Wahab Hasbullah University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32764/saintekbu.v13i01.2523

The success of pesantren education institutions can be measured by the success of their students. By predicting the possible outcomes of the learning process based on prediction results can help an Islamic boarding school, by adjusting the factors that contribute and influence the success rate of students' performance studies. And by utilizing data mining techniques that can be used to increase the level of success and reduce the failure of students. this can greatly help pesantren educational institutions to improve their graduates 'skills, because data mining is the best solution to find hidden patterns and can predict the success of students' performance studies. This research presents a model based on decision tree classification algorithm C 5.0 used in this model with alumni tracer study filled by santri alumni. In this study also used the k-folds cross validation test scenario with k values of 2,3,6,10 and 15 with a total of 300 alumni data and 84 data used for validation tests without cross validation. Determination of the criteria for the classification results using a confusion matrix form the measurement of the classification results obtained, namely the highest value in this study is 95% resulting from 15 folds the scenario 1. And form the results of testing the validation data without cross validation, the corresponding results are 73.81%, when compared to the k-folds, there was an increase of 21.19% and it can be ignored that the C 5.0 algorithm is able to classify well. So that pesantren educational institutional can provide a foundation in the arrangement for their students in deciding the right school choice.

Komparasi Algoritma Naive Bayes dan K-Nearest Neighbor untuk Membangun Pengetahuan Diagnosa Penyakit Diabetes Nurmalasari, Maulidya Dwi; Kusrini, Kusrini; Sudarmawan, Sudarmawan
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 1 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i1.5140

Diabetes is caused by a deficiency of the hormone insulin, which is secreted by the pancreas to lower blood sugar levels. The factors that trigger the occurrence of diabetes are derived from various factors such as a combination of genetic and environmental factors. The phenomenon of the emergence of various beverage brand outlets can be one of the triggers for blood sugar levels in humans. Normal blood sugar levels in the body range from 70-130 mg/dL before eating, less than 180 mg/dL two hours after eating, less than 100 mg/dL after not eating or surviving for eight hours, and 100-140 mg/dL at bedtime. This research aims to determine which algorithm is suitable for building knowledge about diabetes using the Naïve Bayes and K-Nearest Neighbor (KNN) algorithm. The accuracy results from Naïve Bayes are 85.60% and K- Nearest Neighbor of 91.61%. The results showed that K-Nearest Neighbor proved to have the best accuracy.

Pengukuran Kinerja Algoritma K-Means dan Hierarchical Aglomerative Clustering Dalam Pengelompokan Perkara Dispensasi Kawin di Wilayah Pengadilan Tinggi Agama Makassar Slamet, Slamet; Kusrini, Kusrini
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 14, No 4 (2025): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v14i4.8441

Dispensasi kawin atau dispensasi nikah merupakan sebuah upaya bagi masyarakat yang ingin menikah, namun belum memenuhi persyaratan batas usia untuk menikah yang ditetapkan oleh pemerintah, sehingga perlu mengajukan proses dispensasi kawin ke Pengadilan Agama melalui proses persidangan. Melalui algoritma K-Means Clustering dan Hierarchical Aglomerative Clustering dilakukan klasterisasi terhadap data dispensasi kawin di wilayah Pengadilan Tinggi Agama Makassar. Hasil Klasterisasi di evaluasi menggunakan metode Davies-Bouldin Index. Proses klasterisasi yang dilakukan pada data dispensasi kawin Pengadilan Tinggi Agama Makassar, dihasilkan jumlah klaster yang sama dari dua algoritma yang digunakan yakni 4 klaster. Nilai evaluasi terhadap klaster yang dihasilkan oleh dua algoritma yang digunakan, algortima K-Means menghasilkan nilai Davies-Bouldin Index 1,898 dan algoritma Hierarchical Aglomerative Clustering dihasilkan nilai Davies-Bouldin Index 1,906. Mengacu pada nilai evaluiasi Davies-Bouldin Index terhadap dua algoritma tersebut, dikatakan proses klasterisasi yang dihasilkan cukup baik.Kata Kunci: Dispensasi Kawin; K-Means Clustering; Hierarchical Aglomerative Clustering; Pengadilan Agama; Davies-Bouldin Index

Development of an IoT-Based Electric Safety Buoy with Autonomous Navigation System for Coastal Water Rescue Operations Hari Muktafin, Elik; Abdullah Sukri, M Iqbal; Aziz Muzani, Ma'ruf; Sulistiyono, Mulia; Kusrini, Kusrini; Setiaji, Bayu
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11674

This research aimed to develop and evaluate an IoT-based electric safety buoy equipped with an autonomous navigation system to support Search and Rescue (SAR) operations in coastal environments. The system integrates dual‐thruster propulsion, GPS and Inertial Measurement Unit (IMU) sensors, IoT telemetry, and a Return-to-Home (RTH) mechanism, enabling both manual and autonomous operation modes. Prototype testing was conducted in a controlled aquatic environment under light wave conditions (10–25 cm) and mild surface currents (0.18–0.32 m/s), with calm weather and unobstructed line-of-sight communication. The buoy was evaluated in both unloaded and 2 kg payload conditions, traveling at an average speed of 1.25–1.35 m/s across test sessions lasting 12–18 minutes. Three predefined GPS waypoints were used to assess navigation accuracy, motion stability, RTH reliability, and telemetry performance. Results show that the autonomous mode achieved a mean positioning error of 1.12 m, a cross-track deviation of 0.35 m, and a waypoint success rate of 96%, outperforming manual navigation by 52%. The RTH function maintained a success rate of 100% under low-battery conditions and 92% during communication loss, while IoT telemetry remained stable up to 200 meters with less than 1% packet loss. These findings confirm that integrating IoT-based telemetry with adaptive autonomous navigation enhances rescue mission efficiency and operational safety, while indicating the need for further validation under more challenging open-sea conditions.

Deep Learning-Based Soybean Leaf Disease Classification Using DenseNet121, Xception, and MobileNetV2 Helmawati, Nita; Buana, Yopy Tri; Darmawan, Eko Rahmad; Kusrini, Kusrini
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6271

This study is driven by the challenge of soybean leaf diseases, which significantly reduce agricultural productivity and pose a threat to food security. To address this issue, we developed a deep learning–based classification model for soybean leaf disease detection, employing three prominent architectures: DenseNet121, Xception, and MobileNetV2. The dataset comprised 770 images representing six disease categories and one healthy category, which was expanded to 5,880 images using data augmentation techniques. The dataset was evaluated under three experimental scenarios with splits of 70% training, 10% validation, and 20% testing. Experimental results demonstrated that the DenseNet121 model, optimized with AdamW, achieved the highest accuracy at 90.14%, outperforming MobileNetV2 (85.48%) and Xception (65.37%). Moreover, DenseNet121 exhibited the most consistent performance in classifying the diverse categories of soybean leaf diseases.

Co-Authors AA Sudharmawan, AA Abdillah, Yahya Auliya Abdullah Sukri, M Iqbal Abdullah, Mochamad Fadillah Achmad Oddy Widyantoro Ade Pujianto, Ade Adhani, Muhammad Azmi Agastya, I Made Artha agung budi AGUS PURWANTO Ahmad Yusuf Aji Santoso, Bayu Aji Susanto Anom Purnomo Alfatta, Hanif Alva Hendi Muhammad Andi Muhammad Irfan Andi Sunyoto Andika, Roy Andriyanto, Rifki Angga Kurniawan Anggit Dwi Hartanto, Anggit Dwi Anggraeni, Meita Dwi Ardana, Wildan Muhammad Ardana, Wildan Muhammmad Ardiansyah, Fachri Ari Yuana, Kumara Arief Setyanto Arief, M Rudyanto Arief, Muhammad Rudyanto Arifuddin, Danang Arik Sofan Tohir Aris Subadi Arli Aditya Parikesit Asnawi, Muhamad Fuat Atin Hasanah Azi, Amanda Aziz Muzani, Ma'ruf Aziz, Moh Abdul Azkar, Azkar Bayu Setiaji Béjar, Rodrigo Martínez Bentar Candra P Bernadhed, Bernadhed Bisono, Hadi Hikmadyo Braeken, An Buana, Yopy Tri Candra, Kurnia Khoirul da Silva, Bruno Darmawan, Eko Rahmad David Agustriawan DHANI ARIATMANTO Dzulhijjah, Dwi Ahmad Eko Pramono Eko Purwanto Ema Utami Emha Taufiq Luthfi Fatkhurrochman, Fatkhurrochman Fauzi, Moch Farid Fauzy, Marwan Noor Febrianti, Winda Febriyanti, Nada Rizki Ferry Wahyu Wibowo fitriyanto, nur Gifari, Okta Ihza Halimi, Ahmad Hamdikatama, Bimantyoso Hanafi Hanafi Hanif Al Fatta Hari Muktafin, Elik Haris, Ruby hartanto, david budi Hartono, Anggit Dwi Haryo, Wasis Hasan, Nur Fitrianingsih Hasan, Nurul Rahmawati Helmawati, Nita Herawati, Maimi Heri Abijono, Heri Herlinawati, Noor Hulvi, Alfajri Ikhwanudin, Aolia Ilmawati, Fahma Inti Jeki Kuswanto Juwariyah, Siti Kasman, Haris Saktiawan Kurniasari, Iin Kusnawi , Kusnawi Kusnawi Kusnawi Lewu, Retzi Y. Linda, Kumara Dewi Listyanto, Ahmad Wildan López, Alba Puelles Lukman Bachtiar M. RUDYANTO ARIEF M. Suyanto, M. Madhika, Yudha Randa Mahendra, Awanda Putra Mangun, Syamsul Syahab Maradona, Maradona Mardiana Mardiana Martínez-Béjar, Rodrigo Masruri, Nizar Haris Masud, Ibnu maulana, fahrizal Megantara, Muhamad Arldi MEI PARWANTO KURNIAWAN Metha, Halifa Sekar Miftachuddin, Achmad Agus Athok Mohamad Firdaus, Mohamad Mohammad Diqi Mohammad Rezza Pahlevi Moningka, Nirwan Mufti Ari Bianto Muhamad Iksan, Muhamad Muhammad Resa Arif Yudianto Muktafin, Elik Hari Mulia Sulistiyono Muzakir, Muhammad MZ, Reza Rafiq Nasiri, Asro Ngaeni, Nurus Sarifatul Ni Nyoman Utami Januhari, Ni Nyoman Nugroho, Agung Nugroho, Hanantyo Sri Nuk Ghurroh Setyoningrum Nurmalasari, Maulidya Dwi Oktafiqurahman, Andi Olajuwon, Sayyid Muh. Raziq Onde, Mitrakasih La ode Oscar Samaratungga Pamoengkas, Muhamad Agoeng Pamungkas, Sapto Pradipta, Dody Prameswari, Sonia Anjani Prasetio, Agung Budi Prastyo, Rahmat Pratama, Muhammad Egy Puri, Fiyas Mahananing Purnamasari, Resti Putra, Andriyan Dwi Rachmawati Oktaria Mardiyanto RAMADHAN, SYAIFUL Rasyid, Magfirah Raynald Alfian Yudisetyanto Riduan, Nor Rizkayati, Anisa S, Muhamad Rois S, Muhammad Sabri Saleh, Robby Febrianur Samponu, Yohakim Benedictus Santosa, Hendriansyah SANTRI SANTRI Saputro, Moh. Rizal Bayu Sarawan, Tommy Sari, Yayak Kartika Selvy Megira, Selvy Semma, Andi Bahtiar Sentoso, Thedjo Setiawan, Moh. Arif Ma'ruf Setyanto, Arif Siswo Utomo, Mardi Slamet . Solikin, Arif Fajar Sudarmawan, Sudarmawan Sudarto Sudarto Swastikawati, Claudia Syafutra, Arif Dwi Syaiful Huda Tala, WD. Syarni Tampubolon, Jandri Tamuntuan, Virginia Toifur, Tubagus TONNY HIDAYAT Tri Nugroho, Arief Tukan, Ewaldus Ambrosius Ula, M. Izul Wahyu Pujiharto, Eka Wahyudi, Alfian Cahyo Wangsa, Sabda Sastra Wijaya, Jodi Wiwi Widayani, Wiwi Yanuargi, Bayu Yossy Ariyanto Yuana, Kumara Ari Yuza, Adela Zakaria Zakaria Zuhri, Muhammad Rafli Zulkarnain, Imam Alfath Zumarni, Zumarni

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