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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) KAPAL Jurnal Ilmu Pengetahuan dan Teknologi Kelautan SITEKIN: Jurnal Sains, Teknologi dan Industri Journal of Innovation and Applied Technology Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Mechanical Engineering for Society and Industry Tri Dharma Mandiri: Diseminasi dan Hilirisasi Riset Kepada Masyarakat (Jurnal Pengabdian Kepada Masyarakat)
Kurnianingtyas, Diva
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Aerospace Engineering Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences Transportation Other

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Advanced computational techniques for predicting 3D printing distortion in selective laser melting processes of Aluminium AlSi10Mg Choiron, Moch. Agus; Purnowidodo, Anindito; Zacoeb, Achfas; Setyawan, Gembong Edhi; Wirawan, Willy Artha; Ariadi, Yudhi; Rennie, Allan E.W.; Kurnianingtyas, Diva
Mechanical Engineering for Society and Industry Vol 5 No 1 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/mesi.12581

Abstract

Distortion for 3D printing using Selective Laser Melting (SLM) on AlSi10Mg aluminium is an important issue that affects the final manufactured product. This research aims to develop a finite element method (FEM)-based computational simulation and experimental validation to predict distortion in 3D printed products using SLM. The study results found that the variation of 3D printing position affects the resulting product's distortion and mechanical properties. The 90° part print position results in smaller distortion of 0.303 and 0.335 mm than the 0° part print position of 0.329 and 0.378, respectively, making it more suitable for high-precision applications. This study confirms the importance of scan orientation in controlling distortion in the SLM process, which can be used as a guide for optimal printing parameters. With proper orientation selection, the risk of distortion or defects in SLM products can be minimised, and industrial production efficiency can be improved.
OPTIMASI SELEKSI FITUR MULTI-OBJEKTIF DENGAN NSGA-II UNTUK PREDIKSI GAGAL BAYAR MENGGUNAKAN EXTREME GRADIENT BOOSTING Geoffrey Manurung, Daniel; Kurnianingtyas, Diva; Rahman, Muh. Arif
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 9 No 13 (2025): Publikasi Khusus Tahun 2025
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Naskah ini akan diterbitkan di IEEE Transactions on Evolutionary Computation
Automated menu planning for pregnancy based on nutrition and budget using population-based optimization method Kurnianingtyas, Diva; Daud, Nathan; Arai, Kohei; Indriati, Indriati; Marji, Marji
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3483-3492

Abstract

Nutritional fulfilment during pregnancy depends on the budget. Meanwhile, nutrition is needed during pregnancy to keep the mother and fetus healthy. Therefore, this study aims to assist maternal nutrition planning by using population-based optimization methods such as genetic algorithm (GA), particle swarm optimization (PSO), duck swarm algorithm (DSA), and whale optimization (WO) according to their nutritional needs at minimum cost. Additionally, this study compares the method performance to find the best method. There are 55 foods obtained from previous studies divided into five groups: staple food (SF), vegetables (VG), plant-source food (PS), animal-source food (AS), and complementary (CP). The model evaluation results show that GA's performance differed significantly from other models because it obtained the highest fitness by 439.73 and more variation in fitness results. Three models other than GA have no significant difference, but DSA performance obtained a superior fitness of 367.18. Furthermore, optimization methods must be combined with other artificial intelligence methods to develop innovative technology to support maternal nutrition and prevent stunting.
Comparison Genetics Algorithm and Particle Swarm Optimization in Dietary Recommendations for Maternal Nutritional Fulfillment Kurnianingtyas, Diva; Daud, Nathan; Indriati, Indriati; Muflikhah, Lailil
SITEKIN: Jurnal Sains, Teknologi dan Industri Vol 21, No 2 (2024): June 2024
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/sitekin.v21i2.28937

Abstract

Fulfilling maternal nutrition is an NP-hard problem. Optimization techniques are required to solve its complexity. This issue is crucial as it affects the number of stunted toddlers in Indonesia. Stunting begins in the womb due to inadequate maternal nutrition during pregnancy. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are optimization methods applied to NP-hard problems, including medicine. Their performance has not been compared in this field. This study aims to identify an alternative method for recommending daily menus based on maternal nutritional needs. There are 55 food ingredients used to fulfill five menu parts: staple food (SF), vegetables (VG), plant source food (PS), animal source food (AS), and complementary (CP). Nutritional adequacy for prenatal is determined by Total Energy Expenditure (TEE) based on basal energy, daily activity, and stress levels. Results show PSO outperforms GA in average fitness values, 30.45 to 102.51, while GA excels in execution time, 0.33 to 23.22 seconds. PSO is preferred for effectiveness, and GA for efficiency, but given the problem's urgency, PSO is recommended. Exploring other metaheuristic methods is advised to enhance menu recommendation solutions for maternal nutrition. Additionally, expanding the food database is necessary for more varied maternal menu to support stunting prevention.
Analisis Judul Majalah Kawanku Menggunakan Clustering K-Means Dengan Konsep Simulasi Big Data Pada Hadoop Multi Node Cluster Rahardian, Brillian Aristyo; Kurnianingtyas, Diva; Mahardika, Dyan Putri; Maghfira, Tusty Nadia; Cholissodin, Imam
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 4 No 2: Juni 2017
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1125.356 KB) | DOI: 10.25126/jtiik.201742239

Abstract

AbstrakSaat ini pembaca e-magazine seperti majalah Kawanku semakin marak dan terus berkembang. Sehingga penggunaan data besar sangat dibutuhkan pada majalah Kawanku. Selain itu, dibutuhkan pengkategorian setiap bacaan ke dalam tujuh kategori judul pada majalah Kawanku. Sehingga dibutuhkan suatu pengolahan, pengelompokkan, dan pengkomunikasian antar data teks menggunakan text mining. Kombinasi text mining dengan Big Data dapat menjadi sebuah solusi yang menyediakan cara yang efisien dan reliabel untuk penyimpanan data dan infrastruktur yang efektif. Lalu pengkategorian teks dengan  clustering K-Means dirasa cukup meskipun menggunakan data besar karena hasilnya memiliki keakuratan yang tinggi. Dari hasil pengujian yang dilakukan, disimpulkan bahwa perbedaan dari banyaknya data tidak mempengaruhi waktu eksekusi karena perbedaan jumlah data yang digunakan tidak terlalu besar.Kata kunci: text mining, k-means, hadoop, big data, clustering, multi node cluster AbstractNowadays e-magazine reader like Kawanku magazine are increasing more and more.. So the use of Big Data is needed in managing e-magazine data in server. In addition, it takes the categorization of each reading into 7 categories of Kawanku magazine. So it takes a processing, grouping, and communicating between the text data using text mining. The combination of text mining with Big Data can be a solution that provides an efficient and reliable way for data storage and effective infrastructure. Then the text categorization with K-Means clustering is enough although using Big Data as a result has a high accuracy. From the results of tests performed, it was concluded that the difference of the number of data does not affect the execution time due to differences in the amount of data used is not too big.Keywords: text mining, k-means, hadoop, big data, clustering, multi node cluster
Optimasi Derajat Keanggotaan Fuzzy Tsukamoto Menggunakan Algoritma Genetika Untuk Diagnosis Penyakit Sapi Potong Kurnianingtyas, Diva; Mahmudy, Wayan Firdaus; Widodo, Agus Wahyu
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 4 No 1: Maret 2017
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1285.197 KB) | DOI: 10.25126/jtiik.201741294

Abstract

                Sistem inferensi fuzzy bisa digunakan untuk diagnosis penyakit pada sapi potong. Untuk mendapatkan akurasi yang tinggi maka batasan fungsi keanggotaan fuzzy perlu ditentukan secara tepat. Penggunaan metode logika fuzzy untuk memperoleh hasil diagnosis penyakit pada sapi potong sesuai pakar berdasarkan batasan gejala penyakit dan aturan-aturan yang diperoleh dari pakar. Batasan tersebut bisa diperbaiki menggunakan Algoritma Genetika untuk mendapatkan akurasi yang lebih baik. Pengujian yang dilakukan pada 51 data dari beberapa gejala penyakit menghasilkan akurasi sebesar 98,04% dengan menggunakan parameter genetika terbaik antara lain ukuran populasi sebesar 80, ukuran generasi sebesar 15, nilai Crossover rate (Cr) sebesar 0,9, dan nilai Mutation rate (Mr) sebesar 0,06. Akurasi tersebut mengalami peningkatan sebesar 3,54% sesudah dilakukannya optimasi pada metode logika fuzzy.Kata kunci: diagnosis penyakit sapi potong, logika fuzzy, Algoritma GenetikaAbstract                Fuzzy inference systems can be used to diagnose cattle disease. Prior to obtaining the most accurate of limitation, fuzzy membership functions must be defined precisely. Thus, the limits will be optimized along with Genetic Algorithm to get more accurate results. The function of fuzzy logic methods in the diagnosis of disease is relied upon the parametres set by experts. Tests that were performed on 51 data from some of the symptoms of the disease resulted in an accuracy of 98.04% using the best genetic parameters with the population size of 80, the size of the generation of 15, crossover rate value of 0.9, and the value of mutation rate of 0.06. The accuracy has increased by 3.54% compare to results before optimization. Keywords: cattle disease diagnosis, fuzzy logic, genetic algorithms
Sistem Pendukung Keputusan Diagnosis Penyakit Sapi Potong Menggunakan K-Nearest Neighbour (K-NN) Kurnianingtyas, Diva; Rahardian, Brillian Aristyo; Mahardika, Dyan Putri; A., Amalia Kartika; K., Dwi Angraeni
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 4 No 2: Juni 2017
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (721.164 KB) | DOI: 10.25126/jtiik.201742308

Abstract

AbstrakIndustri peternakan merupakan salah satu industri yang penting dalam bidang penyediaan nutrisi makanan sehingga pertumbuhan produk ternak bisa menciptakan suatu ancaman kesehatan masyarakat dimana menyebabkan permasalahan kesehatan. Kurangnya pengetahuan peternak sapi potong mengenai berbagai penyakit yang menyerang serta solusi penanganan salah satu alasan  memanajemen kesehatan ternak dirasa cukup menyulitkan beberapa peternak. Pengembangan sistem pendukung keputusan yang menggunakan metode K-Nearest Neighbour (K-NN) sebagai metode inferensi untuk mendiagnosis penyakit ini. Data 11 jenis penyakit dapat dikenali oleh sistem pendukung keputusan dan 20 jenis gejala yang dapat dikenali oleh sistem. Hasil pengujian keakuratan 325 data latih dan 11 data uji telah menghasilkan tingkat akurasi 100% dengan nilai k = 3.Kata kunci: penyakit sapi potong, sistem pendukung keputusan, K-Nearest Neighbour AbstractThe livestock industry is one industry that is important in the provision of food nutrients so that the growth of livestock products could create a public health threat which causes health problems. Lack of beef cattle farmers knowledge about the various diseases that attack as well as the handling solutions is one reason s managing animal health are considered difficult for some farmers. The development of decision support systems using K-Nearest Neighbour (K-NN) as an inference method to diagnose this disease. Data 11 types of diseases can be recognized by decision support systems and 20 types of symptoms that can be recognized by the system. Results of testing the accuracy of 325 training data and test data 11 has yielded an accuracy rate of 100% with a value of k = 3.Keywords: cattle cow disease, desicion support system, K-Nearest Neighbour
Deteksi Tingkat Resiko Kanker Serviks pada Wanita Usia Subur dengan Metode Modified K-Nearest Neighbor Mukhrodi, Dillah Lyra; Muflikhah, Lailil; Kurnianingtyas, Diva
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 9 No 5 (2025): Mei 2025
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Kanker serviks merupakan kanker kedua yang paling banyak diderita oleh wanita di Indonesia. Penyebab utama kanker serviks 99,7% berkaitan erat dengan infeksi virus Human Papilloma. Salah satu metode yang digunakan untuk skrining kanker serviks adalah IVA (Inspeksi Visual dengan Asam Asetat), yang berfungsi mendeteksi keberadaan sel-sel abnormal sejak dini. Namun, metode IVA memiliki kekurangan, yakni tingkat sensitivitas dan spesifitas yang rendah. Maka, penelitian ini dilakukan dengan tujuan untuk mendeteksi tingkat resiko kanker serviks dengen menerapkan metode Modified K-Nearest Neighbor (MKNN). Metode tersebut dipilih karena mampu menangani data outlier dan tidak seimbang. Metode ini bekerja dengan menghitung jarak antar data sambil menyesuaikan bobot tetangga berdasarkan jaraknya sehingga menghasilkan klasifikasi yang lebih representatif. Dataset penelitian mencakup 314 pasien kanker serviks. Setelah melalui tahapan data preprocessing, pembagian data yang digunakan selama penelitian, yaitu 824 data latih dan 275 data uji. Penelitian ini melibatkan 7 kelas, yakni stadium IA, IB, IIA, IIB, IIIB, IVA, dan IVB. Kombinasi teknik resampling dan seleksi fitur diterapkan untuk meningkatkan performa model, menghasilkan akurasi tertinggi sebesar 92,72% pada nilai gain 0,07 dan k = 2. Lalu, pengujian kedua dilakukan dengan menerapkan stratified k-fold cross validation menunjukkan rata-rata akurasi 91,99% pada k = 2.
Penjadwalan Ujian Akhir Semester dengan Algoritma Ant Colony Optimization (Studi Kasus: Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Brawijaya) Sukma, Lintang Cahyaning; Kurnianingtyas, Diva; Muflikhah, Lailil
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 9 No 13 (2025): Publikasi Khusus Tahun 2025
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Naskah ini akan diterbitkan di Jurnal Internasional Cogent Engineering 
Penjadwalan Ujian Akhir Semester Menggunakan Algoritma Artificial Bee Colony (Studi Kasus: Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Brawijaya) Bajsair, Fath' Hani Sarli; Kurnianingtyas, Diva; Muflikhah, Lailil
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 9 No 13 (2025): Publikasi Khusus Tahun 2025
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Naskah ini akan diterbitkan di Jurnal Internasional Cogent Engineering
Co-Authors Achfas Zacoeb Adimoelja, Ariawan Agus Wahyu Widodo Agus Wahyu Widodo, Agus Wahyu Anindito Purnowidodo Ariadi, Yudhi Bajsair, Fath' Hani Sarli Bayu Rahayudi Candra Dewi Daud, Nathan Dian Purnamasari Dimas Kurniawan Dyan Putri Mahardika, Dyan Putri Edy Santoso Eko Sakti Pramukantoro, Eko Sakti Fabiano, Stefanus Gembong Edhi Setyawan Geoffrey Manurung, Daniel Hanif, Haidar Huda, Fais Al Ika Amalia Kartika Imam Cholissodin Indriati Indriati Indriati K., Dwi Angraeni Kirana, Naufal Laksana Kohei Arai Lailil Muflikhah Mahmudy, Wayan Marji Marji Moch. Agus Choiron Mohammad, Luthfansyah Muh. Arif Rahman Muhammad Luqman Hakim, Muhammad Luqman Mukhrodi, Dillah Lyra Naufal, Muhammad Jilan Novanto Yudistira Prasetyo, Dimas Fajar Putra, I Gusti Ngurah Mayun Suryatama Giri Rahadi, Cakra W.K. Rahadi, Shinta J.A. Rahardian, Brillian Aristyo Rennie, Allan E.W. Rina Rina Rudianto, Amadeo Muhammad Augie Soebroto, Arief Andi Sukma, Lintang Cahyaning Syafira, Putri Amanda Tusty Nadia Maghfira, Tusty Nadia Virliani, Putri Wayan Firdaus Mahmudy Wirawan, Willy Artha Yulfani, R. D.