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All Journal Jurnal Ilmu Komputer JAIS (Journal of Applied Intelligent System) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Teknologi Sistem Informasi dan Aplikasi J-SAKTI (Jurnal Sains Komputer dan Informatika) JURIKOM (Jurnal Riset Komputer) KOMPUTIKA - Jurnal Sistem Komputer Progresif: Jurnal Ilmiah Komputer Jurnal Manajemen Informatika dan Sistem Informasi Budimas : Jurnal Pengabdian Masyarakat Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informatika: Jurnal Pengembangan IT Science Technology and Management Journal (STMJ) Inovasi Sosial: Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Indonesia
Saraswati, Galuh Wilujeng
Universitas Dian Nuswantoro
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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Decision Support System (DSS) for Rodenticide Selection using the TOPSIS Method Fitasari, Ayu Tri Nur; Lutfina, Erba; Saraswati, Galuh Wilujeng
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.16008

Abstract

Selecting an appropriate rodenticide is a critical decision in pest control operations, as each product differs in effectiveness, application cost, safety level, environmental impact, and resistance potential. In practice, rodenticide selection is often based on technician experience or habitual product use, which may result in subjective and less optimal decisions. This study aims to develop a decision support system for rodenticide selection using the TOPSIS method within a multi-criteria decision-making (MCDM) framework. The evaluation is conducted based on six criteria: effectiveness, application cost, safety derived from LD50 values, secondary poisoning risk, resistance potential, and application convenience. To improve the robustness of the decision-making model, this study incorporates an adaptive TOPSIS approach through scenario-based weighting and compares the results with the Simple Additive Weighting (SAW) method. The findings show that alternatives with a balanced performance in terms of safety and operational cost consistently achieve higher rankings, with Warfarin Bait and Zinc Phosphide appearing as top-performing options across different evaluation scenarios. In addition, the proposed model is implemented in a web-based system using a prototype development approach, enabling automated calculations and transparent ranking results. This study provides a structured and practical decision support model that integrates technical, economic, and environmental considerations to support more objective decision-making in pest control management.
Impementasi Metode SAW pada Sistem Pendukung Keputusan untuk Penentuan Prioritas Pendampingan Bayi Dibawah Dua Tahun di Kota Kediri Wawan Darmawan; Wildan Mahmud; Galuh Wilujeng Saraswati
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9671

Abstract

Determining the priority of assistance for children under two years old (Baduta) is a crucial step in accelerating stunting reduction programs. However, in practice, this process is still conducted manually, leading to potential subjectivity and inaccurate targeting. This study aims to implement the Simple Additive Weighting (SAW) method within a Decision Support System to determine the priority of Baduta assistance in Kediri City in an objective and systematic manner. The dataset consists of 300 Baduta, evaluated based on criteria including body weight, height, attendance at community health posts, and access to health service referrals. Preliminary results indicate that the normalization process successfully transformed the data into a standardized scale (0–1), enabling proportional comparison across all criteria. The weighting process shows that height and body weight have dominant contributions to the preference value calculation. The final results demonstrate that the system produces preference values ranging from 0.46 to 0.83, where lower values indicate higher priority for assistance. Furthermore, the system successfully identifies the top 10 priority Baduta as the primary targets for intervention. The implementation of the system also improves decision-making efficiency compared to manual methods and produces more consistent and objective rankings. The main contribution of this study lies in the integration of the SAW method into a dashboard-based system to support more accurate and measurable decision-making for prioritizing Baduta assistance.
MODEL SISTEM PENDUKUNG KEPUTUSAN HYBRID MCDM (AHP-SAW) UNTUK PENENTUAN PRIORITAS FORMASI JABATAN CALON APARATUR SIPIL NEGARA DAERAH Cahyani, Almaun Tri; Saraswati, Galuh Wilujeng; Mahmud, Wildan
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 11 No 1 (2026): APRIL
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v11i1.66147

Abstract

Penentuan formasi jabatan Calon Aparatur Sipil Negara (CASN) di pemerintah daerah sering bersifat subjektif dan belum terintegrasi dengan data analisis beban kerja dan analisis jabatan. Kondisi ini menyebabkan ketimpangan distribusi pegawai serta ketidakefisienan dalam perencanaan sumber daya manusia. Penelitian ini bertujuan merancang Sistem Pendukung Keputusan (SPK) untuk menentukan prioritas formasi jabatan secara objektif dan berbasis data. Metode yang digunakan adalah pendekatan Multi-Criteria Decision Making (MCDM) dengan mengintegrasikan Analytic Hierarchy Process (AHP) untuk pembobotan tujuh kriteria dan Simple Additive Weighting (SAW) untuk perangkingan 168 alternatif jabatan dari 62 unit organisasi (OPD). Data penelitian bersumber dari analisis jabatan, analisis beban kerja, serta data kepegawaian aktual. Hasil penelitian menunjukkan bahwa model menghasilkan bobot kriteria yang konsisten dengan nilai Consistency Ratio (CR) sebesar 0,0453. Jabatan Pemadam Kebakaran Pemula pada OPD-62 memperoleh nilai preferensi tertinggi (Vi = 0,5973) sebagai prioritas utama. Implementasi sistem dalam bentuk dashboard analitik dapat mendukung pengambilan keputusan yang lebih objektif, transparan, dan berbasis data.
PENERAPAN MODEL DEEP LEARNING BILSTM UNTUK KLASIFIKASI MULTI-KELAS PADA DATA ADUAN MASYARAKAT Garda, Kautsa Adi; Saraswati, Galuh Wilujeng; Lutfina, Erba
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 11 No 1 (2026): APRIL
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v11i1.66293

Abstract

Layanan Pengaduan Masyarakat merupakan suatu kanal layanan yang dipergunakan masyarakat untuk melaporkan suatu kejadian pelanggaran dan masalah yang tidak sesuai dengan aturan tertulis maupun tidak tertulis. Pemerintah Kota Kediri melalui Dinas Komunikasi dan Informatika mempunyai suatu sistem layanan pengaduan online untuk menampung keluhan, saran, serta aspirasi masyarakat. Keberagaman dan tingginya volume aduan menuntut operator untuk meneruskan laporan kepada banyak pilihan instansi terkait sekaligus. Oleh karena itu, perlu adanya sistem yang mampu melakukan klasifikasi otomatis terhadap teks aduan masyarakat. Penelitian ini bertujuan untuk otomatisasi klasifikasi teks aduan menggunakan algoritma Bidirectional Long Short-Term Memory (BiLSTM) dengan skema multiclass sebanyak 21 kelas. Pembagian data dilakukan dengan rasio 80:20 untuk data training dan testing, kemudian 10% dari data pelatihan digunakan sebagai data validation. Penelitian ini menggunakan Word2Vec untuk pembobotan kata, Random Oversampling untuk balancing data serta membandingkan uji parameter batch size dan optimizer. Berdasarkan hasil dari 8 skenario pengujian, diperoleh skenario model dengan akurasi tertinggi sebesar 0,6977 (70%). Nilai tersebut tergolong cukup baik mengingat jumlah kelas yang relatif banyak dan variasi penggunaan bahasa daerah dan slang pada data aduan. Secara keseluruhan, BiLSTM menunjukkan kinerja yang memadai untuk klasifikasi teks multiclass.
Co-Authors Achmad Naila Muna Ramadhani Adelia Rahmawati Adhitya Nugraha Aditya Wahyu Ramadhan Adji, Dian Restu Agus Winarno Ahmad Zainul Fanani Ajib Susanto Akbar Dwi Syahputra Angga Apriano Hermawan Azhara Devi Sandi Azzahra, Tarissa Aura Bagas Aditya Mahendra Cahyani, Almaun Tri Cahyani, Salsabila Nida Caturkusuma, Resha Meiranadi Danny Oka Ratmana Dianna Yanuaresta Didik Hermanto Dwi Puji Prabowo, Dwi Puji Erba Lutfina Etika Kartikadarma Fafaza, Safira Alya Fakhrurrozi Fakhrurrozi, Fakhrurrozi Febrianti, Ervina Febrianto, Nanang Filmada Ocky Saputra Filmada Ocky Saputra Fitasari, Ayu Tri Nur Garda, Kautsa Adi Guruh Fajar Shidik Gustina Alfa Trisnapradika Handoyo, Dhiky Resandi Wur Harisa, Ardiawan Bagus Heru Agus Santoso Iqlima Zahari Joel Justin Adrian Lakui Johary Lutfina, Erba Malik Aziz Ali Mandasari Kusuma Dyah Tantri Mardiantara, Naya Alifiah az Azar Putri Megantara, Rama Aria Meilani Dwi Permatasari Mellati, Pita Miranti Alysha Zulia Larasati Muhamad Ni'am Syukri Roni Asmi Muhammad Syaifur Rohman Muhammad Syaifur Rohman Muljono, - Mulyanto, Edy Nimasari, Azza Nur Inayati Nurun Najmi Amanina Pergiwati, Dewi Permana, Danang Juniar Prashanti, Eva Pratama, Zudha Pulung Nurtantio Andono Rahmat Trinanda Pramudya Amar Rama Tri Agung Ramadhan, Aditya Wahyu Ramadhani, Irfan Wahyu Ratmana, Danny Oka Renjiro Azhar Pramono Resha Meiranadi Caturkusuma Ricardus Anggi Pramunendar Rino Agung Rizky Syah Gumelar Rohman, Muhammad Syaifur Rohman, Muhammad Syaifur Saputra, Filmada Ocky Sri Winarsih, Nurul Anisa Wawan Darmawan Wildan Mahmud Winarsih, Nurul Anisa Sri Winasis, Galih Adi Yustiqomah, Evita Citra Zuhdi, Ahmad Muzaki