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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Setrum : Sistem Kendali-Tenaga-elektronika-telekomunikasi-komputer Intelmatics Muhibbul Arabiyah: Jurnal Pendidikan Bahasa Arab
Binti Solihah
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Computer Science & IT Dentistry Education Languange, Linguistic, Communication & Media Library & Information Science Other

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NORMALISASI DAN PEMBOBOTAN UNTUK KLONING MULUS PADA PENCAMPURAN CITRA MENGGUNAKAN METODE POISSON Shofiati, Ratna; Solihah, Binti; Irmadani, Sari
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 11, No 1, Januari 2013
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1155.241 KB) | DOI: 10.12962/j24068535.v11i1.a14

Abstract

Pada penelitian ini diterapkan normalisasi dan pembobotan pada teknik pencampuran dengan metode Poisson sehingga dapat diperoleh citra hasil pencampuran. Dengan teknik ini, daerah batas antara dua citra yang dicampur dapat menyatu dengan sempurna tanpa harus melakukan penyuntingan gambar secara manual. Citra asal yang akan dicampur ke dalam citra target disegmentasi terlebih dahulu menggunakan metode ambang batas untuk membuat topeng. Metode Poisson diterapkan pada citra masukan dan target untuk mendapatkan nilai yang setelah dinormalisasi ke dalam [0 1] akan menjadi bobot dalam perhitungan nilai piksel baru pada daerah perbatasan antara citra asal dan target. Percobaan yang dilakukan pada sejumlah citra menunjukkan bahwa penggunaan bobot ternormalisasi hasil perhitungan Poisson memberikan efek keabuan yang makin meningkat mendekati batas luar citra asal. Hal ini menunjukkan bahwa prosentase nilai piksel pada batas terluar citra asal lebih besar dibandingkan dengan daerah batas bagian dalam. Perhitungan Kloning Mulus yang dilakukan dengan pembobotan tersebut menghasilkan gambar asal yang menyatu secara sempurna dengan citra target.
Analisis Kinerja Model Pengontrol Ekson DNA Menggunakan Metode Model Hidden Markov Suhartati Agoes; Binti Solihah; Alfred Pakpahan
Setrum : Sistem Kendali-Tenaga-elektronika-telekomunikasi-komputer Vol 3, No 2 (2014): Edisi Desember 2014
Publisher : Fakultas Teknik Elektro - Universitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36055/setrum.v3i2.500

Abstract

urutan Deoxyribo asam nukleat (DNA) yang memiliki beberapa bagian ekson dalam urutan coding (cd) adalah bagian penting dalam proses biologis untuk menghasilkan protein. Tujuan dari penelitian ini adalah untuk mengontrol ekson DNA yang ada di CD dengan menggunakan Hidden Markov Model (HMM) sehingga protein yang dihasilkan tidak berubah. HMM metode memiliki parameter misalnya; negara, nilai keadaan transisi, negara emisi dasar dan algoritma yang digunakan untuk pelatihan dan proses pengujian. Nilai dari negara transisi secara acak berbagai ditentukan nilai antara 0 ~ 1. Pelaksanaan HMM di ekson kontroler memiliki struktur model 20-negara dan tes simulasi dilakukan dengan menggunakan nilai negara transisi dan jumlah urutan yang berbeda. Proses simulasi dengan struktur model 20-negara adalah menghasilkan nilai kinerja model dengan Koefisien Korelasi (CC) adalah 0,7571 dengan menggunakan 220 urutan. Penelitian ini meningkatkan nilai CC dengan cara mengelompokkan data dan hasilnya adalah 0,8808 untuk sub model dengan 69 urutan dan 0,8183 dengan 157 urutan.
Visualisasi Kinerja dan Persepsi Peserta Program Bangkit 2021 Menggunakan Microsoft Power BI Dedy Sugiarto; Rianti Dewi Sulamet-Ariobimo; Binti Solihah; Ahmad Zuhdi; Ratna Shofiati; Anung Barlianto Ariwibowo; Teddy Siswanto; Dimmas Mulya
Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Vol 13, No 1 (2022): Juni
Publisher : Universitas Bandar Lampung (UBL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jsit.v13i1.2311

Abstract

Penelitian ini bertujuan untuk membangun visualisasi kinerja dan persepsi peserta program Bangkit 2021 Fakultas Teknologi Industri Universitas Trisakti dalam bentuk dasbor. Data berasal dari respon kuesioner peserta Bangkit 2021 terkait dampak program MBKM, data transkrip mahasiswa yang diperoleh dari penyelenggaraan Program Bangkit dan data indeks prestasi mahasiswa yang didapatkan dari sistem informasi akademik (student information system) Universitas Trisakti. Pemodelan data menggunakan model skema bintang dengan tiga tabel fakta yaitu tabel nilai mata kuliah yang diikuti, tabel kehadiran dan status lulus serta tabel kuesioner. Tabel dimensi terdiri atas dimensi jalur pembelajaran, dimensi program studi, dimensi mata kuliah. Hasil visualisasi menunjukkan laporan kinerja dan persepsi peserta dapat dengan mudah dan singkat dilihat dalam masing-masing satu layar yang dapat disaring berdasarkan dimensi program studi, jalur pembelajran maupun mata kuliah yang diikuti. Secara umum 75% dinyatakan lulus penuh (full) dan 25 % lulus sebagian (parsial) serta salah seorang peserta berhasil mendapatkan predikat 50 tim terbaik. Seluruh peserta juga menyatakan kegiatan ini bermanfaat bagi mereka untuk meningkatkan keterampilan dan keahlian serta meningkatkan kemampuan bekerja sama dalam sebuah tim.
An ANALYSIS OF OIL SENTIMENT SENTIMENTS ON TWITTER USING SUPPORT VECTOR MACHINE: ANALISIS SENTIMEN SUBSIDI BAHAN BAKAR MINYAK (BBM) DI TWITTER MENGGUNAKAN SUPPORT VECTOR MACHINE Ibnu Bilal Marta Prawira; Binti Solihah; Syandra Sari
Intelmatics Vol. 3 No. 1 (2023): Januari-Juni
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/itm.v3i1.16187

Abstract

Twitter is one of the social media platforms used by people in Indonesia. Twitter is often used by its users to express opinions regarding a product, institution or event. From the keyword fuel, fuel subsidy is a keyword that is currently a trending topic because changes in fuel subsidies affect the prices of other staples, to find out the value of sentiment in public opinion, sentiment analysis is one of the methods used is the support vector machine and lexicon based. Lexicon is a labeling method by matching the words contained in the document with the words contained in the dictionary. After labeling, the data is tested using the classification method, the classification stage is carried out after going through the preprocessing phase, where the tweet classification results tend to be positive or negative, using the Support Vector Machine method and validated by K-Fold Cross Validation.This research produced 50,001 data which were divided into 21,561 positive sentiments, 9206 neutral sentiments and 19234 negative sentiments. From these results it can be concluded that the data shows public support for rising fuel prices or changing fuel subsidy prices.
Analysis Of Topic Movement & Conversation Membership On Twitter Using K-Means Clustering Sediyono, Agung; Valentino Hutagalung, Josua; Solihah, Binti
Intelmatics Vol. 4 No. 2 (2024): Juli-Desember
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v4i2.21002

Abstract

Humans are born to socialize with each other. Social media is one of the media to be able to socialize with each other. Twitter is one of the social media that contains hundreds of millions of tweets where the tweet contains news, products that are currently popular, even about the daily life of users who can change. Social Context Analysis is a tool to analyze social changes and individual needs in society from time to time. In this study, the author uses the K-means Clustering method to group topics on Twitter. In its implementation, this research is expected to be able to see the occurrence of topic movements and membership movements on Twitter topics.
Peningkatkan Kemampuan Menghafal Kosa-Kata Bahasa Arab Dengan Penerapan Metode Bernyanyi: Improve The Ability To Memorize Arabic Vocabulary With The Application Of The Singing Method Hanafi, Ahmad; Solihah, Binti
MUHIBBUL ARABIYAH: Jurnal Pendidikan Bahasa Arab Vol. 2 No. 1 (2022): MUHIBBUL ARABIYAH: Jurnal Pendidikan Bahasa Arab
Publisher : Himpunan Mahasiswa Program Studi (HMPS) Pendidikan Bahasa Arab Fakultas Tarbiyah dan Ilmu Keguruan (FTIK) Institut Agama Islam Negeri (IAIN) Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35719/pba.v2i1.22

Abstract

This research is motivated by the lack of ability of students in class XI IIK MA Assalam Jambewangi-Blitar for the 2021 academic year in understanding Arabic learning materials. Because the previous teacher has never implemented new methods to overcome problems. Therefore, the understanding of the Arabici learning material which is felt that it is difficult for students to remain there. For this reason, the appropriate solution is needed to overcome the problem is by applying the singing mthod in improving the ability of students to understand Arabic lessons, namely by increasing memorizing the vocabulary of Arabic. These vocabulary are the basic of students to learn Arabic. This research is a qualitative descriptive approach. Observation, interviews, test, questionnaires and documentation is the method of data collection. This researchis a class action research model. This purpose of class action research is to improve and solve practical to improve the quality of education and foster proactive attitudes in the school environment to make improvements and mutual learning.This method is very interesting so that with the implementation the result of the ability to memorize the vocabulary of students can be improved and easy to understand Arabic materials. Penelitian ini dilatarbelakangi oleh kurangnya kemampuan siswa kelas XI IIK MA Assalam Jambewangi-Blitar tahun ajaran 2021 dalam memahami materi pembelajaran bahasa Arab. Karena pengajar sebelumnya belum pernah menerapkan metode-metode baru untuk mengatasi permasalahan tersebut. Oleh karena itu, pemahaman  materi pembelajaran bahasa Arab yang dirasakan siswa sulit tetap ada. Untuk itu, diperlukan solusi yang tepat untuk mengatasi permasalahan tersebut yaitu dengan menerapkan metode bernyanyi dalam meningkatkan kemampuan siswa untuk memahami pelajaran bahasa Arab yaitu dengan meningkatkan hafalan kosa-kata bahasa Arab siswa. Kosa-kata ini menjadi dasar siswa untuk mempelajari bahasa Arab. Penelitian ini merupakan pendekatan deskriptif kualitatif.  Observasi, wawancara, tes, angket dan dokumentasi merupakan metode pengumpulan datanya. Penelitian ini merupakan model penelitian tindakan kelas. Tujuan dari penelitian tindakan kelas  yaitu untuk melakukan perbaikan dan memecahkan persoalan-persoalan praktis untuk  meningkatkan kualitas pendidikan dan menumbuhkan sikap proaktif lingkungan sekolah untuk melakukan perbaikan dan mutu pembelajaran secara berkelanjutan. Metode ini sangat menarik sehingga dengan adanya penerapan tersebut alhasil kemampuan hafalan kosa-kata bahasa Arab siswa dapat ditingkatkan dan mudah dalam memahami materi bahasa Arab
Analisis Sentimen dan Pemodelan Topik Ulasan PengunjungObjek Wisata Pulau Bali pada Situs Tripadvisor MenggunakanMetode Lexicon-Based dan Latent Dirichlet Allocation (LDA) Aulia, Muhammad Azka; Solihah, Binti; Zuhdi, Ahmad
Intelmatics Vol. 5 No. 1 (2025): January-June
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v5i1.17619

Abstract

One sought-after type of information by internet users is related to tourist destinations. Hence, the need for information retrieval about a particular tourist spot they plan to visit. This study aims to analyze sentiments and identify the topics in the visitor reviews of Bali Island tourist attractions on TripAdvisor using Lexicon-based and Latent Dirichlet Allocation (LDA) methods. The data used for analysis consists of reviews from various tourist destinations on the island of Bali. For sentiment analysis, the author employs a Lexicon-based approach, focusing on both positive and negative sentiments. To identify the topics in the reviews, the author employs the LDA method to uncover the most frequently discussed topics. From 15,827 dataset, It is found that 87,6% of the responses are positive, 7.9% are negative, and the remaining 4.4% are neutral. As for the topic modeling results, the study identifies four main topics with the best coherence values based on the validation of topics with topic coherence. These four topics are: the first topic discusses experiences in Safari or Safari Park in Bali, the second topic talks about experiences in tourism in Kintamani, Bali, the third topic focuses on experiences in tourism in Nusa Penida, Bali, and the last topic discusses experiences in Scuba Diving activities
PERFORMANCE COMPARISON OF TWITTER SENTIMENT ANALYSIS USING FASTTEXT SVM AND TF-IDF SVM: A CASE STUDY ON ELECTRIC MOTORCYCLES Sulaba, Wishnu Abhinaya; Solihah, Binti; Sari, Syandra
Intelmatics Vol. 4 No. 2 (2024): Juli-Desember
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v4i2.18145

Abstract

Electric motorcycles are trending on Twitter as two-wheeled vehicles different from those using fossil fuels. Electric motorcycles rely on batteries charged using electricity. However, there are many opinions about electric motorcycles on social media, especially Twitter. Yet, tweets and comments on Twitter often contain irrelevant words that can affect sentiment analysis. In this study, sentiment analysis was conducted on 8,000 data from Twitter using FastText and TF-IDF as word embedding techniques, along with Support Vector Machine (SVM) as the classification technique. The aim of this research is to compare the performance of SVM using different feature extraction techniques, namely FastText and TF-IDF. The results of this study are expected to be beneficial for electric vehicle manufacturers and individuals interested in electric vehicles. In this comparison, the performance of TF-IDF and FastText feature extraction in sentiment classification with SVM will be evaluated. SVM performance is assessed based on accuracy, precision, recall, and F1-score for each feature extraction technique used. The test results show an average accuracy above 83%, with the highest values being 86% for accuracy, 79% for precision, 52% for recall, and 58% for F1-score.  
COMPARATIVE SENTIMENT ANALYSIS OF VISITOR REVIEWS FOR WATERBOM BALI TOURIST ATTRACTION ON TRIPADVISOR SOCIAL MEDIA USING RANDOM FOREST AND NAÏVE BAYES CLASSIFICATION Hilmi, Hilmi Abdul Gani; Solihah, Binti; Sari, Syandra
Intelmatics Vol. 4 No. 2 (2024): Juli-Desember
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v4i1.19278

Abstract

With the advancement of technology, especially the internet, the role of the internet as the primary source of information in global life is becoming increasingly crucial. This is particularly true in the context of searching for information about tourist destinations before visiting them. TripAdvisor is a website designed for searching travel destinations and attractions. On this platform, users can provide reviews and see comments from other travelers regarding various tourist destinations, including Waterbom Bali. To gain insights into visitors' perspectives and enhance services for them, the overwhelming number of reviews can be analyzed for sentiment to understand whether travelers' views tend to be positive, negative, or neutral. In this research, the Random Forest and Naïve Bayes methods are employed to conduct sentiment analysis. Scraping data from Waterbom Bali resulted in a dataset of 5750 entries. Despite data imbalance after labeling positive, negative, and neutral sentiments, class imbalance techniques will be applied. The sentiment analysis method, comparing Random Forest and Naïve Bayes, is implemented using the Word2Vec feature extraction method to evaluate its effectiveness. Experimental results show significant differences between the two methods. In Random Forest, after undersampling, an accuracy of 24% was obtained, while oversampling resulted in an accuracy of 98%. Meanwhile, for Multinomial Naïve Bayes, after undersampling, an accuracy of 36% was achieved, and oversampling yielded an accuracy of 97%.
The Opportunity of Ai Technology to Increase The Value Chain of Oil Palm Plantation Sediyono, Agung; Solihah, Binti
Intelmatics Vol. 5 No. 1 (2025): January-June
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v5i1.22477

Abstract

Indonesia produced 58,4% of worldwide oil palm production, and the contribution of the plantation sub-sector in 2022 is 3,76% of PDB and 30,32% of the Agriculture, Forestry, and Fishery sectors. However, oil palm production in Indonesia is lack of productivity and efficiency compared to other countries, especially Malaysia.  Therefore, this paper tries to explore the opportunities of AI technology to increase the value chain of the oil palm plantation, especially in productivity and efficiency. The scope of exploration started from oil palm seeding, nursery,  planting,  and harvesting. Based on the oil palm plantation value chain review and the previous research works in AI implementation on value chain respectively, it can be concluded that AI technology has been explored to be implemented in oil palm plantations intensively. However, there is still enough room for improvement especially in accuracy rate and adoption feasibility for smallholder planters. Moreover, IoT and drone technology have a big potential to be adopted because the plantation is mostly hard-to-reach areas by humans, for instance high oil palm bunch, long distance journey for inspection and maintenance, wild animal threat, etc.  
Co-Authors Ahmad Hanafi Ahmad Zuhdi Ahmad Zuhdi Alfred Pakpahan Anung B Ariwibowo Aulia, Muhammad Azka Dedy Sugiarto Dimmas Mulya Hilmi, Hilmi Abdul Gani Ibnu Bilal Marta Prawira Ratna Shofiati Rianti Dewi Sulamet-Ariobimo Sari Irmadani Sari, Syandra Sediyono, Agung Suhartati Agoes Sulaba, Wishnu Abhinaya Syaifudin Syandra Sari Teddy Siswanto Valentino Hutagalung, Josua Wibowo, Nurafni Revita