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Pengaruh Perbandingan Komposisi Ampas Tebu dan Kulit Durian Terhadap Karakteristik Bio-oil Iriany; Maulisa; Salsabila; Taslim
Jurnal Teknik Kimia USU Vol. 14 No. 2 (2025): Jurnal Teknik Kimia USU
Publisher : Talenta Publisher (Universitas Sumatera Utara)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jtk.v14i2.16294

Abstract

The bio-oil from bagasse has poor physical characteristics, so it requires a combination with other materials, such as durian peel. This study aims to produce high-quality bio-oil from the best composition ratio of bagasse and durian peel. The study used variations of sugarcane bagasse and durian peel compositions (0:1; 1:0; 1:1; 2:1; 3:1) at a pyrolysis temperature of 400 °C for 5, 6, and 7 hours using an 83.1 L upright cylindrical reactor. The bio-oil with the best characteristics was found at a 1:1 ratio, with yields of 32.28%, 38.82%, and 40.8%, pH values of 2.7; 2.8; 2.9, and viscosities of 0.952 cSt, 0.928 cSt, and 0.908 cSt. The results show that adding durian peel improves the bio-oil characteristics.
Optimasi Nilai k Pada Algoritma k Nearest Neighbor Untuk Prediksi Akademik Mahasiswa Yang Bekerja Taslim; Yuhelmi; Toresa, Dafwen
The Indonesian Journal of Computer Science Vol. 10 No. 2 (2021): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v10i2.3005

Abstract

Sebuah lembaga pendidikan akan selalu fokus bagaimana meningkatkan kualitas akdemik dari peserta didik mereka. Penelitian ini bertujuan untuk melakukan klasifikasi dan prediksi terhadap prestasi akademik mahasiswa terutama bagi mahasiswa yang bekerja karena mereka mempunyai beban yang lebih dibanding mahasiswa yang tidak bekerja. Hasil dari prediksi ini selanjutnya dapat digunakan sebagai salah satu bahan pertimbangan bagi pihak akademik dalam mengambil kebijakan terhadap mahasiswa yang sudah bekerja. Prediksi prestasi akademik dilakukan dengan menggunakan algoritma K nearest neighbor dengan optimasi pada nilai k dengan algoritma k-fold cross validation dengan 5-fold cross validation. Kelas label terdiri atas 3 kategori yaitu memuaskan, sangat memuaskan dan dengan pujian. Dari hasil penelitian didapat nilai k= 3. Uji akurasi performance menghasilkan nilai sebesar 85,71%.
Optimasi Nilai K Pada Algoritma k-Means untuk Klasterisasi Data Pasien Covid-19 Moh. Fatkuroji; Fajrizal; Taslim; Eka Sabna; Kursiah Warti Ningsih
The Indonesian Journal of Computer Science Vol. 11 No. 2 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i2.3088

Abstract

With the spread of Covid-19 to various countries, it is difficult for Governments and Health Agencies in the world to handle Covid-19 cases to date. The prevention carried out by the Government and Health Agencies in the world is carried out by giving vaccines to the public. However, in some places it is not implemented in accordance with PMK Number 84 of 2020 which prioritizes providing vaccines to the elderly. With the current density of the population in Indonesia, the administration of vaccines does not see who is prioritized first. The application of the k-means algorithm is carried out to cluster patients affected by Covid-19 on the Covid-19 case data obtained from kaggle.com in the form of patient data from January 1, 2020 to May 31, 2020 as many as 139119 cases. The results of clustering data on cases affected by Covid-19 with k=3 yielded a WCSS value of 6801292.2. Calculations of the K-Means Algorithm using the Google Collaboratory Tools resulted in clusters with the cases of patients affected by Covid-19 in Cluster-0 as many as 58.237 cases, in Cluster-1 as many as 53.932 cases, and in Cluster-2 as many as 26.950 cases.