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All Journal JURNAL SISTEM INFORMASI BISNIS Semantik INFOKAM Scientific Journal of Informatics JIEET (Journal of Information Engineering and Educational Technology) Transformasi Router : Jurnal Teknik Informatika dan Terapan JIPITI: Jurnal Pengabdian kepada Masyarakat Jurnal Sistem Informasi (JASISFO) Router : Jurnal Teknik Informatika dan Terapan
Kartika Imam Santoso
STMIK Bina Patria
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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SISTEM INFORMASI AJUAN KREANOVA DI BAPPEDA DAN LITBANGDA KABUPATEN MAGELANG Santoso, Kartika Imam; Yusuf , Hikmaturridho; Wahyudiono, Sugeng
Jurnal Sistem Informasi (JASISFO) Vol. 4 No. 2 (2023): September 2023
Publisher : Politeknik Negeri Sriwijaya

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Abstract

Penelitian ini bertujuan agar dapat merancang dan membangun Sistem Informasi Ajuan Kreanova Berbasis Web Di Bappeda Dan Litbangda Kabupaten Magelang.  Pemrograman yang digunakan adalah bahasa pemrograman PHP dan DBMS nya adalah MySQL. Sistem informasi ini sebagai media informasi dan pelayanan pengajuan inovasi (Kreanova) sehingga memudahkan proses pengajuan dan mengelola data Kreanova. Metode pengembangan sistem yang digunakan adalah metode pengembangan sistem Waterfall. Perancangan yang digunakan pada penelitian ini adalah pemodelan DFD (Data Flow Diagram). Hasil penelitian ini adalah berupa Sistem Informasi Ajuan Kreanova Berbasis Web di Bappeda Dan Litbangda Kabupaten Magelang untuk memudahkan proses pengajuan dan mengelola data data Kreanova.
Expert System For Corn Plant Disease Diagnosis Using Hybrid Fuzzy Tsukamoto And Naive Bayes Method Kartika Imam Santoso; Eko Supriyadi; Andri Triyono; Dhika Malita Puspita
JIEET (Journal of Information Engineering and Educational Technology) Vol. 9 No. 2 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v9n2.p141-155

Abstract

Corn is a strategic food commodity in Indonesia, with production of 22.44 million tons in 2023. However, disease attacks can cause productivity declines of up to 30-80%, mainly from downy mildew, leaf rust, and leaf spot. The limited number of pathology experts in the field leads to delayed diagnosis, resulting in significant economic losses for farmers. This research aims to develop an expert system for diagnosing corn plant diseases using a hybrid Fuzzy Tsukamoto and Naive Bayes method to enhance diagnosis accuracy, taking into account uncertainty in symptom severity levels. The system was developed using Durkin's Expert System Development Life Cycle (ESDLC), which consists of six phases. A knowledge base was built from SINTA and Scopus-indexed literature, identifying five diseases and 17 symptoms. The fuzzy Tsukamoto method was employed for the fuzzification of symptom severity, utilizing three membership functions (intensity, coverage, and severity), after which Naive Bayes calculated the posterior probability. The hybrid score was calculated with 40% Fuzzy and 60% Bayes weights. The system was successfully developed with an interactive web interface. Accuracy testing using 30 validation cases yielded an accuracy of 86.67%, with 85% sensitivity and 88% specificity. Expert testing by three plant pathology experts gave excellent ratings (average 4.6/5.0) for diagnosis accuracy, knowledge base completeness, and usability aspects. The hybrid Fuzzy Tsukamoto and Naive Bayes method is effective for diagnosing corn plant diseases, achieving 86.67% accuracy, which is 6.67% higher than the Certainty Factor method and 11.67% higher than the single Naive Bayes method. This system can help farmers perform early diagnosis and reduce dependence on experts.
Klasterisasi Daerah Rawan Demam Berdarah Dengue (DBD) menggunakan Algoritma K-Means di Purwodadi Grobogan Elsa Syahriza Putri; Andri Triyono; Kartika Imam Santoso
Router : Jurnal Teknik Informatika dan Terapan Vol. 4 No. 1 (2026): Maret : Router : Jurnal Teknik Informatika dan Terapan
Publisher : Asosiasi Profesi Telekomunikasi dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/router.v4i1.846

Abstract

Dengue fever is a disease commonly found in tropical and subtropical regions. This disease can cause severe symptoms, such as very high fever, accompanied by nausea, vomiting, headache, abdominal pain, and leukopenia (decrease in white blood cells). This infectious disease, known as dengue hemorrhagic fever (DHF), is a viral infection transmitted by the Aedes Aegyppti mosquito. This study aims to classify dengue-prone areas using the K-Means Algorithm, and to classify the factors that cause dengue in Purwodadi District, Grobogan Regency. The clustering results using the K-Means algorithm with Rapidminer tool from 266 data produced 3 clusters: cluster 0 (blue) with 138 patients dominated by Kuripan, Purwodadi, Ngambak villages, cluster 1 (green) with 31 patients in Ngraji, Nambuhan, Cingkrong villages, and cluster 2 (orange) with 97 patients in Danyang, Kalongan, Pulorejo villages. This study is expected to provide additional information for stakeholders in controlling dengue cases and increase awareness of the importance of environmental cleanliness as a preventive measure.
Klasterisasi Daerah Rawan Demam Berdarah Dengue (DBD) menggunakan Algoritma K-Means di Purwodadi Grobogan Elsa Syahriza Putri; Andri Triyono; Kartika Imam Santoso
Router : Jurnal Teknik Informatika dan Terapan Vol. 4 No. 1 (2026): Maret : Router : Jurnal Teknik Informatika dan Terapan
Publisher : Asosiasi Profesi Telekomunikasi dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/router.v4i1.846

Abstract

Dengue fever is a disease commonly found in tropical and subtropical regions. This disease can cause severe symptoms, such as very high fever, accompanied by nausea, vomiting, headache, abdominal pain, and leukopenia (decrease in white blood cells). This infectious disease, known as dengue hemorrhagic fever (DHF), is a viral infection transmitted by the Aedes Aegyppti mosquito. This study aims to classify dengue-prone areas using the K-Means Algorithm, and to classify the factors that cause dengue in Purwodadi District, Grobogan Regency. The clustering results using the K-Means algorithm with Rapidminer tool from 266 data produced 3 clusters: cluster 0 (blue) with 138 patients dominated by Kuripan, Purwodadi, Ngambak villages, cluster 1 (green) with 31 patients in Ngraji, Nambuhan, Cingkrong villages, and cluster 2 (orange) with 97 patients in Danyang, Kalongan, Pulorejo villages. This study is expected to provide additional information for stakeholders in controlling dengue cases and increase awareness of the importance of environmental cleanliness as a preventive measure.
Co-Authors Al Munawar, Habib Riziq Anak Agung Gede Sugianthara Andri Triyono Arif Ikhsanudin Arum, Dhika Malita Puspita Chrisna Hidayat Cisilia Sundari Dhika Malita Puspita Eko Sediyono Eko Supriyadi Elsa Syahriza Putri Estu Tunjung Pambayun Farida Yunita Iis Mariam Kapti . Mendi Sangkoyrala Hidayat Muhamad Nur Rais, Muhamad Nur Muhammad Abdul Muin Muhammad Ilyas Nafi Projo Kusumo, Nafi Projo Nugroho, Agus Susilo Prasetyo Nugroho Prihati . Putri Kusumasari, Putri Rahayu, Sari Rosita Erfiyana Satriya, Ilham Jati Sri Waluyo Sugeng Wahyudion0 Sugeng Wahyudiono, Sugeng Sukris Sutiyatno Sunarni Sunarni Triyono, Andri Widorani, Efta Yeza Febriani Yusuf , Hikmaturridho