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All Journal Jurnal Ilmiah Teknik Industri ARIKA Archipelago Engineering
Wilma Latuny
Universitas Pattimura
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

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Journal : Archipelago Engineering

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MEMPREDIKSI HARGA JUAL RUMPUT LAUT KERING PADA TINGKAT PETANI DENGAN DATA MINING Wilma Latuny
ALE Proceeding Vol 2 (2019): Archipelago Engineering (ALE)
Publisher : Fakultas Teknik Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/ale.2.2019.186-199

Abstract

Abstrak Eucheuma cotonii sebagai salah satu jenis rumput laut kering cottonii (Dried Euchema Seaweed (DES)) adalah salah satu komoditas perikanan budidaya utama di Indonesia. Petani lokal menanam, memanen, dan mengeringkan rumput laut, dan kemudian menjualnya ke pedagang. Harga jual rumput lauttergantung pada faktor internal dan eksternal. Untuk memaksimalkan keuntungan mereka, para petani harus memperkirakan perkembangan harga di masa mendatang dengan menggunakan faktor-faktor ini. Penelitian ini menyajikan metode berbasis data baru untuk memperkirakan harga DES di masa depan untuk membantu petani dalam membuat prediksi tersebut. Dalam metode kami , kami menerapkan data mining untuk tugas memprediksi harga jual rumput laut pada saat penjualan, delapan minggu ke depan. Algoritma data mining, yaitu, penggolong, membutuhkan atribut sebagai inputnya. Dalam percobaan kami, kami mengidentifikasi tiga faktor internal dan tiga faktor eksternal sebagai atribut masukan. Atribut internal faktor yang digunakan adalah: harga DES terkini, kadar kebersihan dari DES, dan kadar air dari DES. Tiga eksternal-faktor atribut semua berhubungan dengan cuaca dan suhu minimum, suhu maksimum, dan curah hujan yang. Semua atribut yang diukur setiap hari di hari d. Output dari classifier adalah prediksi, klasifikasi biner yang menunjukkan apakah rumput laut harga jual pada waktu d ditambah delapan minggu lebih besar atau lebih kecil dari harga pada saat itu d.
STATIC AND DYNAMIC CUES TO MALE ATTRACTIVENESS Wilma Latuny
ALE Proceeding Vol 1 (2018): Archipelago Engineering (ALE)
Publisher : Fakultas Teknik Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/ale.1.2018.190-197

Abstract

Abstract Most studies on facial attractiveness have relied on attractiveness judged from photographs rather than video clips. Only a few studies combined images and video sequences as stimuli. In order to determine static and dynamic cues to male attractiveness, we perform behavioural and computational analyses of the Mr. World 2014 contestants. We asked 365 participants to assess the attractiveness of images or video sequences (thin slices) taken from the profile videos of the Mr. World 2014 contestants. Each participant rated the attractiveness on a 7-point scale, ranging from very unattractive to very attractive. In addition, we performed computational analyses of the landmark representations of faces in images and videos to determine which types of static and dynamic facial information predict the attractiveness ratings. The behavioural study revealed that: (1) the attractiveness assessments of images and video sequences are highly correlated, and (2) the attractiveness assessment of videos was on average 0:25 point above that of images. The computational study showed (i) that for images and video sequence, three established measures of attractiveness correlate with attractiveness, and (ii) mouth movements correlate negatively with attractiveness ratings. The conclusion of the study is that thin slices of dynamical facial expressions contribute to the attractiveness of males in two ways: (i) in a positive way and (ii) in a negative way. The positive contribution is that presenting a male face in a dynamic way leads to a slight increase in attractiveness rating. The negative contribution is that mouth movements correlate negatively with attractiveness ratings.
PREDIKSI FITUR KEMASAN PRODUK MINYAK KAYU PUTIH DENGAN SUPPORT VECTOR MACHINE (SVM) Wilma Latuny; Victor O. Lawalata; Daniel B. Paillin; Rahman Ohoirenan
ALE Proceeding Vol 4 (2021): Archipelago Engineering (ALE)
Publisher : Fakultas Teknik Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/ale.4.2021.76-82

Abstract

UD Sinar Baru has eucalyptus oil products with various sizes from 30 ml to 550 ml, and the size of 550 ml is the most consumed eucalyptus oil product. However, this product has been criticized by consumers for its packaging which has not met their expectations. This study aims to obtain an accurate method of classifying consumer sentiment and obtain features that affect the redesign of the 550 ml eucalyptus oil product packaging. Collecting data using an online survey method from social media Facebook to get consumer comments using power queries. Data analysis uses the concept of the Support Vector Machine (SVM) method with the support of the WEKA application to provide sentiment analysis and accuracy of consumer comments. The results of the study present the tendency of comments on each attribute with an assessment of 83% accuracy for the entire class, 3% for positive class comments, and 57% comments for negative class. The sentiment that shows the packaging tends to be normal at 20% which is interpreted as neutral. The conclusion from the results of this study is that SMO has a very accurate prediction rate to analyze consumer sentiment about the features of the 550 ml eucalyptus oil packaging, and it is necessary to redesign the current packaging by considering the features of shape, color, size, and efficiency.
Co-Authors Daniel B. Paillin Daniel Bunga Pailin Rahman Ohoirenan Rahman Ohoirenan Victor Oryon Lawalata