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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi Elkom: Jurnal Elektronika dan Komputer CESS (Journal of Computer Engineering, System and Science) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jesya (Jurnal Ekonomi dan Ekonomi Syariah) SOSCIED Jurnal Ilmiah Intech : Information Technology Journal of UMUS J-SAKTI (Jurnal Sains Komputer dan Informatika) Seminar Nasional Ilmu Terapan Journal of Computing Theories and Applications Journal of Future Artificial Intelligence and Technologies
Kristiawan Nugroho
Universitas Stikubank
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Social Sciences

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Journal : Jurnal Ilmiah Intech : Information Technology Journal of UMUS

 1 
Perancangan Sales Prediction Model Menggunakan Metode Neural Network Kristiawan Nugroho; Wiwien Hadi Kurniawati; Raden Mohamad Herdian Bhakti
Jurnal Teknik Informatika UMUS Vol 4 No 02 (2022): November
Publisher : Universitas Muhadi Setiabudi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46772/intech.v4i02.870

Abstract

Datamining merupakan fenomena penting pada bidang ilmu teknologi informasi yang telah membantu manusia pada berbagai bidang kehidupan. Datamining merupakan bidang ilmu yang menarik untuk diteliti apalagi pada saat ini dimana Big Data yang dihaslilkan dalam berbagai kehidupan manusia mempunyai volume yang sangat besar namun kurang memberikan arti bagi kehidupan. Penelitian datamining mengenai sales prediction memberikan kontribusi positif bagi para pengambil keputusan dalam melakukan prediksi penjualan barang yang dilakukan secara online berdasarkan beberapa fitur antara lain usia,jenis kelamin,minat,impresi maupun jumlah uang yang dibelanjakan. Penelitian ini berkontribusi dalam membangun sebuah model regresi sales prediction menggunakan metode Neural Network yang dapat dipergunakan sebagai alat bantu dalam pengambilan keputusan untuk menjual jenis produk yang diminati berbagai macam segment pada toko online. Metode Neural Network yang merupakan salah satu metode yang bekerja berdasar pola berpikir syaraf manusia terbukti memberikan hasil terbaik dalam membangun model sales prediction dibandingkan metode Random Forest dan AdaBoost. Sales prediction model menggunakan Neural Network menunjukkan hasil kinerja yang diukur dengan Mean Squared Error (MSE) sebesar 0.831, Root Mean Square Error (RMSE) sebesar 0,911 dan Mean Absolute Error (MAE) sebesar 0,650.
Prediksi Ujaran Kebencian Berbasis Text Pada Sosial Media Menggunakan Metode Neural Network Kristiawan Nugroho; Endang Tjahjaningsih; Lie Liana; Raden Mohamad Herdian Bhakti
Jurnal Teknik Informatika UMUS Vol 5 No 1 (2023): Mei
Publisher : Universitas Muhadi Setiabudi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46772/intech.v5i1.1063

Abstract

Currently information technology has helped in various forms of human life. They can communicate with each other through various electronic media, including using social media. The number of social media users is increasing from year to year in Indonesia. However, the development of the use of social media has also resulted in various problems, including hate speech, which will eventually lead to legal consequences. Various methods have been taken to limit the development of hate speech, including by blocking users who write hate speech on social media applications. Limiting the use of social media for hate speech can be more optimally carried out by detecting text-based words that have the potential to become hate speech. This study uses the Neural Network (NN) method to predict words that contain hatespeech on social media with an accuracy rate of 73% better than other methods such as Decission Tree and K-Nearest Neighbor (KNN) which only achieve an accuracy rate of 68.5 %.
Co-Authors Ahmad Rofiqul Muslikh Ajeng Aquinia Aliyatul Munna Arnold Adimabua Ojugo De Rosal Ignatius Moses Setiadi De Rosal Igniatus Moses Setiadi Edy Winarno Eko Prasetyo Endang Tjahjaningsih Eri Zuliarso Isworo Nugroho Kasmari . Kristhoporus Hadiono Kristophorus Hadiono Lie Liana Lie Liana . Mamet Adil Araaf Niken Puspitasari Omar Farooq Raden Mohamad Herdian Bhakti Raden Mohamad Herdian Bhakti Suhana Suhana Sunardi Sunardi Syahroni Wahyu Iriananda Wiwien Hadi Kurniawati Yoga Ryan Fatony