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All Journal Indonesian Journal on Software Engineering (IJSE) MULTINETICS Jurnal Riset Multidisiplin dan Inovasi Teknologi
Suseno, Kheri Agus
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Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Engineering Languange, Linguistic, Communication & Media Social Sciences

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Revisiting Trilateration Method Based on Time-of-Flight Measurements for Navigation Simamora, Yohannes S.M.; Rachmach, Nahdia Fadilatur; Rizqon, Muhammad Yaasir; Suseno, Kheri Agus; Hilmi, Muhammad Nursyams
Jurnal Riset Multidisiplin dan Inovasi Teknologi Том 2 № 01 (2024): Jurnal Riset Multidisiplin dan Inovasi Teknologi
Publisher : PT. Riset Press International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59653/jimat.v2i01.432

Abstract

This paper revisits trilateration in three-dimensional positioning. Specifically, range between a positioning target and the reference points through time-of-flight (ToF) measurements. In a ToF, range is yielded by multiplying the time required by a wave to travel between two points and its propagation speed. Position of the target can be then estimated once the number of references are adequate, i.e. at least three for two-dimensional positioning and four for three-dimensional one. In this paper, the positioning is considered for navigation where the target moves following a trajectory whilst the ToFs take place in a certain period. The target position at the time is computed based on the ToFs through least square estimation. Through a numerical simulation, it is shown that the trilateration can track a target’s trajectory despite the decreasing performance at the end of the course.
KLASIFIKASI SAMPAH PLASTIK BERDASARKAN DETEKSI WARNA RGB DENGAN METODE K-NEAREST NEIGHBOR Suseno, Kheri Agus; Faqih, Husni
Indonesian Journal on Software Engineering Vol 10, No 2 (2024): IJSE 2024
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijse.v10i2.25671

Abstract

Pengelolaan sampah plastik merupakan sebuah langkah yang sangat dibutuhkan kalangan masyarakat saat ini dalam menentukan penanganan yang tepat terutama pada sampah plastik. Mengelompokkan beberapa jenis plastik atau klasifikasi jenis plastik PET, HDPE, PP, LDPE PVC dan Other akan sangat membantu dalam pengelolaan sampah plastik terutama ketepatan dalam menentukkan jenis-jenis plastik tersebut agar lebih mudah dalam penanganannya. Dari jenis plastik pada umumnya dibagi menurut sifat khusus dan jenis bahannya sebagai pembeda sesuai kategori plastik tersebut. Dari jenis masing-masing plastik dapat diklasifikasikan dari beberapa fitur warna, berat dan sifat kimia yang terkandung didalamnya[2]. Penentuan warna dari jenis plastik ditentukan dengan menggunakan alat deteksi warna RGB dan T (Red Green Blue Temperatur Warna). Sensor Warna yang menghasilkan gelombang frekuensi pantulan sehingga didapatkan berupa data numerik tertentu sehingga dapat dijadikan sebagai penentu sifat, dan warna serta kualitas plastik tersebut. Nilai dari hasil deteksi ini akan dikumpulkan menjadi sebuah datasheet private yang akan digunakan sebagai penentu dari jenis-jenis plastik yang ada. Metode yang digunakan untuk menentukkan jenis plastik ini menggunakan metode K-nearest neighbor (KNN).               Kata kunci: Klasifikasi Pengelolaan sampah plastik, PET, HDPE, PP, LDPE PVC dan Other, Artivicial K-nearest neighbor (KNN).
Studi Komparatif Metode Naive Bayes dan Support Vector Machine dalam Menganalisis Sentimen Ulasan Ask-AI Faqih, Husni; Aji, Sopian; Suseno, Kheri Agus
MULTINETICS Vol. 11 No. 1 (2025): Vol. 11 No. 1 (2025): MULTINETICS Mei (2025)
Publisher : POLITEKNIK NEGERI JAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32722/multinetics.v11i1.7534

Abstract

The development of Artificial Intelligence (AI) has brought significant changes in the field of information and communication. The Ask-AI application is popular and has many reviews on the Google Play Store platform. The purpose of this study is to analyze user review sentiments towards the Ask-AI application and compare the performance of two text classification algorithms, namely Naive Bayes and Support Vector Machine (SVM) in classifying reviews into positive and negative sentiment categories. A total of 628 reviews were used as a dataset consisting of 314 positive reviews and 314 negative reviews. The dataset has gone through a text preprocessing stage including letter transformation (transform cases), tokenize, common word removal (stopword removal), and dictionary-based stemming. Data analysis using RapidMiner software and for model performance evaluation using the k-fold cross-validation approach which can provide more stable and representative results for the entire data. The evaluation results produce a performance value of the SVM algorithm which has very good performance. SVM produces an accuracy of 94.08%, a precision of 96.23%, a recall of 92.31%, and an Area Under Curve (AUC) value of 0.981. Meanwhile, the Naive Bayes algorithm provides an accuracy of 78%, a precision of 85.23%, a recall of 68.37%, and an AUC of 0.801. The results of the study indicate that the SVM method is superior to Naïve Bayes in classifying the sentiment of Ask-AI application user reviews because it can provide more accurate, consistent, and more sensitive classification results to variations in text data. It is hoped that this study can be a reference for choosing the optimal sentiment classification algorithm for AI-based application user review data.
Co-Authors Aji, Sopian Faqih, Husni Hilmi, Muhammad Nursyams Rachmach, Nahdia Fadilatur Rizqon, Muhammad Yaasir Yohannes S.M. Simamora