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All Journal TEKNIK INFORMATIKA Seminar Nasional Aplikasi Teknologi Informasi (SNATI) JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri Prosiding Seminar Nasional Teknoka JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Informatika Universitas Pamulang Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science bit-Tech Zonasi: Jurnal Sistem Informasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) JUKI : Jurnal Komputer dan Informatika TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Sisfo: Jurnal Ilmiah Sistem Informasi JIKA Journal Of Artificial Intelligence And Software Engineering Jurnal Pengembangan Teknologi Informasi dan Komunikasi (JUPTIK) Solusi Bersama : Jurnal Pengabdian Dan Kesejahteraan Masyarakat Indonesian Journal of Innovation Multidisipliner Research
Iis Afrianty
Universitas Islam Negeri Sultan Syarif Kasim Riau
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Journal : journal of artificial intelligence and software engineering

 1 
Information Gain and Random Forest for Sex Classification Based on Craniometric Measurements Nabilla Alya Firana; Iis Afrianty; Novriyanto Novriyanto; Febi Yanto
Journal of Artificial Intelligence and Software Engineering Vol 6, No 2 (2026): Juni (OnProgress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i2.9409

Abstract

Sex identification from human skulls is a crucial aspect of forensic anthropology; however, traditional methods still face limitations such as subjective assessment and inter-population variation. This study proposes the application of Information Gain as a feature selection technique and Random Forest as a classification algorithm for sex determination based on craniometric data. The dataset used is the Howells dataset consisting of 2,524 samples with 83 skull measurement features. Feature selection using Information Gain was performed with threshold values of 0.01, 0.05, and 0.09, followed by additional testing across a threshold range of 0.01 to 0.09. Model evaluation was conducted using 10-Fold Cross Validation with default Random Forest parameters. The results show that a threshold of 0.02 produced 57 selected features from the original 83, achieving the best performance with an accuracy of 87.40%, precision of 87.53%, recall of 87.40%, and F1-score of 87.41%. These results outperform the baseline model without feature selection, which achieved an accuracy of 86.57%. This study demonstrates that Information Gain feature selection can reduce data dimensionality by 31.3% while simultaneously improving sex classification performance based on craniometric data.
Application of Information Gain Feature Selection and SMOTE in XGBoost Algorithm for Asthma Disease Classification Fioni Nikmatul Fajar; Fitri Insani; Suwanto Sanjaya; Iis Afrianty
Journal of Artificial Intelligence and Software Engineering Vol 6, No 2 (2026): Juni (OnProgress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i2.9384

Abstract

Asma merupakan salah satu penyakit kronis pada sistem pernapasan yang prevalensinya terus meningkat dan memerlukan deteksi dini untuk mencegah komplikasi serius. Salah satu tantangan dalam klasifikasi asma menggunakan machine learning adalah ketidakseimbangan kelas yang menyebabkan model cenderung memprediksi kelas mayoritas sehingga kemampuan mendeteksi kasus asma menjadi rendah. Penelitian ini mengusulkan penerapan SMOTE dan seleksi fitur Information Gain dalam algoritma XGBoost untuk mengatasi permasalahan tersebut. Dataset yang digunakan terdiri dari 2.392 data dengan 28 atribut, di mana tahapan penelitian meliputi preprocessing, seleksi fitur menggunakan Information Gain yang mengurangi fitur menjadi 22 fitur, penyeimbangan data menggunakan SMOTE, pembagian data dengan rasio 90:10, 80:20, dan 70:30, serta klasifikasi menggunakan XGBoost. Pengujian dilakukan terhadap empat skenario pendekatan untuk membandingkan kontribusi setiap metode yang diterapkan. Evaluasi dilakukan menggunakan data uji seimbang dan data uji asli dengan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa skenario terbaik diperoleh pada kombinasi Information Gain + SMOTE + XGBoost dengan rasio 90:10 pada data uji seimbang, menghasilkan akurasi 75%, presisi 87,5%, recall 58,33%, dan F1-score 70%. Hasil tersebut menunjukkan bahwa kombinasi seleksi fitur dan penyeimbangan data mampu meningkatkan kemampuan model dalam mendeteksi penyakit asma.
Co-Authors Agung Surya Maulana Alwaliyanto Alwaliyanto Alwis Nazir Alwis Nazir Alwis Nazir Alwis Nazir Aqshol Al Fahrozi Arif Mudi Priyatno Boni Iqbal Darmila Dewi Nasien Dinyah Fithara efni humairah Eka Pandu Cynthia Eka Pandu Cynthia Ekawati Saputri Ekawati Saputri, Saputri Elin Haerani Elin Haerani Elin Haerani Elvia Budianita Elvia Budianita Erni Rouza, Erni Evodius Nasus Fadhilah Syafria Faizah Husniah Fakhri Fakhri Febi Yanto Felian Nabila Fioni Nikmatul Fajar Fitra Lestari Fitri Insani Fitri Insani Fratiwi Rahayu grace tedy tulak Gusti, Siska Kurnia Habibollah Haron Ihlal Hanafi Harahap Ikhsanul Hamdi Irfan Jamal Matondang Ismail marzuki Iwan Iskandar Iwan Iskandar Iwan Iskandar Jasril Jasril Karina Julita Lestari Handayani M. Aufaa Rahman Mawadda Warohma Mentari Aulia Putri Muhammad Fikry Muhammad Fikry Muhammad Habib Nazlis Muhammad Irsyad Nabilla Alya Firana Nada Tsawaabul Khair Nazruddin Safaat H Negara, Benny Sukma Novriyanto Novriyanto Nurafni Syahfitri Nurul Fatiara Purnama, Nilam Puspa Melani Almahmuda Qurrata A'yuni Rahayu Anggraini Rahmad Abdillah Rahmad Kurniawan Rahmat Al Hafiz Ramadhani Herfin Ravi Umbara Reski Mai Candra Restu Kharrisa Andini Riyan Wibowo Saputra Robby Azhar Sahrul Poalahi Salu Said Nurfan Hidayad Tillah Salmiyati Salmiyati Sephia Pratista Sonya Aulia Febriana SURYA ADITYA GD Surya Agustian Suswantia Andriani Suwanto Sanjaya Syaiful Azhar Ummy Agustina Putri Yusra Yusra