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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) TEKMAPRO Journal of Industrial Engineering and Management REKAYASA Jurnal Ilmiah Pangabdhi JURNAL PENDIDIKAN SAINS DAN APLIKASINYA Matrik : Jurnal Manajemen dan Teknik Industri Produksi Global Science Education Journal
Ari Basuki
Universitas Trunojoyo Madura
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Physics Social Sciences Other

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Rekomendasi Pemilihan Supplier pada UMKM Produsen Tas menggunakan metode AHP – PROMETHEE Ari Basuki; Andharini Dwi Cahyani
Rekayasa Vol 12, No 2: Oktober 2019
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/rekayasa.v12i2.19764

Abstract

UD Sumber Rejeki merupakan UMKM yang memproduksi tas handbag sebagai produk utama. Dalam proses produksi, salah satu kendala utama yaitu keterlambatan bahan baku. Keterlambatan bahan baku diakibatkan oleh kesalahan dalam pemilihan supplier. Pemilihan supplier merupakan salah satu faktor penting berjalannya sebuah perusahaan. Ketika bahan baku sudah terpenuhi dengan baik, maka proses produksi bisa berjalan. Pemilihan supplier memerlukan kriteria dalam mendukung keputusan di dalamnya. Maka dari itu digunakan metode AHP dalam menentukan kriteria dan bobot di dalamnya. Dan digunakan metode PROMETHEE untuk mendapatkan perangkingan hasil kinerja supplier. Didapatkan dari lima supplier langganan, Supplier yang menjadi rekomendasi kepada UD Sumber Rejeki adalah supplier dengan kinerja terbaik yang sudah dinilai dengan AHP dan PROMETHEE. Supplier tersebut adalah supplier 3 (Aini) dengan nilai net flow sebesar 0,5438, S2 (Prima) sebesar 0,4099, S4 (Akbar) sebesar 0,0438. Ketiga supplier ini merupakan supplier prioritas yang diusulkan kepada UD Sumber Rejeki dan Supplier 3 memiliki kinerja terbaik yang akan dijadikan prioritas utama.
Metode Line Balancing Heuristik untuk Penyeleseian Masalah Terjadinya Bottleneck pada Lintasan Produksi Ari Basuki; Andharini Dwi Cahyani
Rekayasa Vol 13, No 3: December 2020
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/rekayasa.v13i3.19765

Abstract

Meningkatnya jumlah Work In Process (WIP) pada lantai produksi atau bottleneck merupakan salah satu permasalahan yang bisa menyebabkan terjadinya keterlambatan suplai produk kepada konsumen. Dampak lain dari terjadinya bottleneck yaitu dapat menyebabkan penumpukan bahan baku setengah jadi pada lantai produksi sehingga WIP menjadi tinggi. Kondisi ini merupakan kondisi merugikan yang harus dihindari oleh perusahaan. Tujuan dari penelitian ini adalah memberikan usulan perbaikan pada permasalahan terjadinya bottleneck di lintasan produksi dengan menggunakan beberapa metode line balancing heuristik, yaitu metode Helgesson-Birnie / Ranked Positional Weight (RPW), metode Region Approach, metode Largest Candidate Rule dan metode J-Wagon yang diterapkan di perusahaan manufaktur (PT.’X’) yang memproduksi pallet. Hasil penelitian diketahui bahwa pada penerapannya metode J-Wagon terpilih sebagai metode yang paling optimal diantara metode lainnya tersebut. Rekomendasi untuk PT.’X’ dalam mengatasi permasalahan bottleneck yaitu dengan pembentukan stasiun kerja yang awalnya memiliki jumlah 5 stasiun kerja diubah menjadi 4 stasiun kerja. Ini menghasilkan perbaikan lini lintasan kerja yang awalnya memiliki line efficiency sebesar 69%, balance delay sebesar 31%, smoothness index sebesar 9.22 dan total waktu menganggur sebesar 3.60 menit menjadi line efficiency sebesar 86%, balance delay sebesar 14%, smoothness index sebesar 0.80 dan total waktu menganggur sebesar 1.28 menit.
Sentiment Analysis of Customers’ Review on Delivery Service Provider on Twitter Using Naive Bayes Classification Ari Basuki
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.26327

Abstract

Customer evaluations on social media may help us remain competitive and comprehend our business's target market. By analysing consumer evaluations, a business owner can identify common themes, pain points, and desired features or enhancements.  By analysing customer feedback across multiple channels, such as social media, online reviews, and customer service interactions, businesses can rapidly identify any negative sentiment or potential brand damage. The contribution of our study is to evaluate the performance of the Naive Bayes method for classifying customer feedback on courier delivery services obtained via Twitter. The Naive Bayes algorithm is selected due to its simplicity, which facilitates efficient computation, suitability for large datasets, outstanding performance on text classification, and ability to manage high-dimensional data. In this investigation, the Naive Bayes classifier accuracy is 0.506, which is considered to be low.  According to our findings, the irrelevant feature classification resulting in an error throughout the categorization process. A large number of data appearance characteristics that do not correspond to the testing data category have been identified as a result of this occurrence.
Pemurnian Garam dengan Metode Rekristalisasi di Desa Bunder Pamekasan untuk Mencapai SNI Garam Dapur Umam, Faikul; Basuki, Ari; Adiputra, Firmansyah
Jurnal Ilmiah Pangabdhi Vol 5, No 1: April 2019
Publisher : LPPM Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (551.749 KB) | DOI: 10.21107/pangabdhi.v5i1.5161

Abstract

Automatic essay scoring: leveraging Jaccard coefficient and Cosine similarity with n-gram variation in vector space model approach Dwi Cahyani, Andharini; Fathoni, Moh. Wildan; Rachman, Fika Hastarita; Basuki, Ari; Amin, Salman; Khotimah, Bain Khusnul
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.pp3599-3612

Abstract

Automated essay scoring (AES) is a vital area of research aiming to provide efficient and accurate assessment tools for evaluating written content. This study investigates the effectiveness of two popular similarity metrics, Jaccard coefficient, and Cosine similarity, within the context of vector space models (VSM) employing unigram, bigram, and trigram representations. The data used in this research was obtained from the formative essay of the citizenship education subject in a junior high school. Each essay undergoes preprocessing to extract features using n-gram models, followed by vectorization to transform text data into numerical representations. Then, similarity scores are computed between essays using both Jaccard coefficient and Cosine similarity. The performance of the system is evaluated by analyzing the root mean square error (RMSE), which measures the difference between the scores given by human graders and those generated by the system. The result shows that the Cosine similarity outperformed the Jaccard coefficient. In terms of n-gram, unigrams have lower RMSE compared to bigrams and trigrams.
Sentiment Analysis of Customers’ Review on Delivery Service Provider on Twitter Using Naive Bayes Classification Basuki, Ari
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.26327

Abstract

Customer evaluations on social media may help us remain competitive and comprehend our business's target market. By analysing consumer evaluations, a business owner can identify common themes, pain points, and desired features or enhancements.  By analysing customer feedback across multiple channels, such as social media, online reviews, and customer service interactions, businesses can rapidly identify any negative sentiment or potential brand damage. The contribution of our study is to evaluate the performance of the Naive Bayes method for classifying customer feedback on courier delivery services obtained via Twitter. The Naive Bayes algorithm is selected due to its simplicity, which facilitates efficient computation, suitability for large datasets, outstanding performance on text classification, and ability to manage high-dimensional data. In this investigation, the Naive Bayes classifier accuracy is 0.506, which is considered to be low.  According to our findings, the irrelevant feature classification resulting in an error throughout the categorization process. A large number of data appearance characteristics that do not correspond to the testing data category have been identified as a result of this occurrence.
Co-Authors Amin, Salman Andharini Dwi Cahyani Andharini Dwi Cahyani Bain Khusnul Khotimah Eka Mala Sari Rohman Faikul Umam Fathoni, Moh. Wildan Fika Hastarita Rachman, Fika Hastarita Firmansyah Adiputra Ichdal Chusnayaini Weny Findiastuti Yeni Kustiyahningsih Yuli Fidiana