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SINTECH (Science and Information Technology) Journal
Published by STMIK STIKOM Indonesia
ISSN : 25987305     EISSN : 25989642     DOI : -
Core Subject : Science,
Computer Science & IT
SINTECH (Science and Information Technology) Journal merupakan jurnal yang dikelola dan diterbitkan oleh Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK STIKOM Indonesia, dengan e-ISSN 2598-9642 dan p-ISSN: 2598-7305. SINTECH Journal diterbitkan pertama kali pada bulan April 2018 dan memiliki periode penerbitan sebanyak dua kali dalam setahun, yaitu pada bulan April dan Oktober. Bidang keilmuan dari SINTECH Journal mencakup bidang ilmu : Data analysis, Natural Language Processing, Artificial Intelligence, Neural Networks, Pattern Recognition, Image Processing, Genetic Algorithm, Bioinformatics/Biomedical Applications, Biometrical Application, Content-Based Multimedia Retrievals, Augmented Reality, Virtual Reality, Information System, Game Mobile, dan IT Bussiness Incubation.
Arjuna Subject : -
Articles 166 Documents
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Komparasi Naïve Bayes dan SVM Analisis Sentimen RUU Kesehatan di Twitter Widyanto, Tetrian; Ristiana, Ina; Wibowo, Arief
SINTECH (Science and Information Technology) Journal Vol. 6 No. 3 (2023): SINTECH Journal Edition December 2023
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v6i3.1433

Abstract

This research focuses on sentiment analysis regarding the plan to ratify the Health Bill which has become a hot topic of conversation on social media, especially Twitter. This research aims to classify tweets that reflect various opinions regarding the Health Bill, including support, rejection and neutrality. In this research, the author uses two types of classification algorithms, namely the Multinomial Naïve Bayes Algorithm and the Support Vector Machine (SVM) Algorithm. Previously, tweets were labelled using the Lexicon InSet dictionary. The research was conducted in the Python programming language and using Google Collaboratory. Data validation was carried out using the K-fold cross-validation method. The research results indicate that both algorithms predominantly produce positive sentiments over negative ones. However, SVM with a linear kernel achieves a higher accuracy rate of 0.87, compared to Multinomial Naïve Bayes, which has an accuracy of 0.82. SVM also records a precision of 0.87, recall of 0.97, and an F1-score of 0.91, while Multinomial Naïve Bayes shows a precision of 0.81, recall of 0.98, and an F1-score of 0.89. Overall, this research confirms that SVM excels in text sentiment classification, while Multinomial Naïve Bayes also provides good results in recognising positive and negative sentiment. These results have important implications for understanding public sentiment regarding the Health Bill on the Twitter platform.
Recognizing Hotel Visitors Preferences Based on Service Consumption Level Using K-Means Method Saraswati, Ni Wayan Sumartini; Bisena, I Kadek Agus; Muku, I Dewa Made Krishna; Krisna, Gede Gana Eka
SINTECH (Science and Information Technology) Journal Vol. 6 No. 3 (2023): SINTECH Journal Edition December 2023
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v6i3.1443

Abstract

Consumer segmentation is an old issue that remains interesting to study today, given the magnitude of the benefits obtained when consumers can be segmented properly. Marketing cost efficiency is one of the great benefits of this process. Likewise, the effectiveness of marketing activities to maintain customer retention. It is because companies can better identify consumers. Based on the hotel service consumption level, this research could identify consumer clusters based on hotel consumer preferences. Thus, hotel management could target specific types of service promotion better and on target. This research built a hotel visitor clustering model using the K-Means Clustering method to determine customer segments based on the level and type of hotel service consumption. The K-Means model was built based on hotel visitor consumption data for each type of service. Furthermore, the hotel visitor clusters formed were identified by their characteristics. Four consumer clusters were obtained based on the silhouette score analysis, which described the characteristics of consumers in each cluster.
Digitalisasi Prasasti dan Pelinggih Desa Baturan Gianyar Berbasis Augmented Reality Based Marker Sarasvananda, Ida Bagus Gede; Aditama, Putu Wirayudi; Iswara, Ida Bagus Ary Indra; Desnanjaya, I Gusti Made Ngurah
SINTECH (Science and Information Technology) Journal Vol. 6 No. 3 (2023): SINTECH Journal Edition December 2023
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v6i3.1448

Abstract

Digitalization of the inscriptions and shrines of Batuan Gianyar Village based on Augmented Reality (AR) Markers is an innovative project that aims to preserve, promote and revive the cultural and historical heritage of Baturan Village, Gianyar, Indonesia, through the application of Augmented Reality technology. Baturan Village is known to have inscriptions and pelinggih which have high historical value. This research uses AR Marker technology to connect the physical world with digital content. The inscriptions and shrines of Baturan Village are marked with AR markers which allow users to access additional information about these objects via mobile devices. This AR application provides an immersive and interactive experience, allowing users to explore the history and meaning of inscriptions and shrines. The method used in developing this AR application is R & D. The results of this research after testing loading time using four different smartphones, Xiaomi Note8 Pro and iPhone 13, have a faster average loading time response compared to other smartphones.
Klasifikasi Jajanan Khas Bali Untuk Preservasi Pengetahuan Kuliner Lokal Menggunakan Arsitektur VGG-16 Rahayu, Ni Luh Widi; Gunantara, Nyoman; Sudarma, Made
SINTECH (Science and Information Technology) Journal Vol. 7 No. 1 (2024): SINTECH Journal Edition April 2024
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v7i1.1455

Abstract

Pemanfaatan teknologi Deep Learning khususnya teknologi pengenalan gambar merupakan suatu media yang tepat digunakan untuk melakukan klasifikasi citra digital. Pada penelitian ini bertujuan untuk melakukan klasifikasi citra jajanan khas Bali dengan mencari model terbaik dari arsitektur VGG-16 dengan melakukan perbandingan tingkat akurasi, recall, precission dan f1-score dari skenario pengujian yaitu bobot dropout, jumlah batch size dan jumlah epoch yang digunakan. Dataset training yang digunakan dalam penelitian ini sebanyak 2.445 data citra jajanan khas Bali dengan total kelas sebanyak 10 kelas yaitu Klepon 320 citra, Laklak 207 citra, Kaliadrem 222 citra, Jaje Lukis 327 citra, Jaje Batun Bedil 189 citra, Pisang Rai 200 citra, Jaje Piling-piling 234 citra, Jaje Wajik 241 citra, Ongol-ongol 308 citra dan Bubur Injin 197 citra. Dataset sebanyak 50 data citra jajanan khas Bali pada setiap kelasnya dengan jumlah total 500 data citra. Model terbaik yang didapatkan dari arsitektur VGG-16 dalam melakukan klasifikasi jajanan khas Bali yaitu dengan tingkat akurasi sebesar 97,5%, presisi 87,9%, recall 87% serta f1-score sebesar 87,4% dengan parameter pengujian dropout 20%, batch size 64 serta epoch 1000 pada data citra uji diluar data pelatihan dan validasi.
Perbandingan Kinerja Algoritma K-Means dan K-Medoids Dalam Klasterisasi Jumlah Tindak Pidana Kejahatan Berbasis Wilayah Kepolisian Daerah Nurcahya, Gelar; Wibowo, Arief; Kristanto, Dwi
SINTECH (Science and Information Technology) Journal Vol. 6 No. 3 (2023): SINTECH Journal Edition December 2023
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v6i3.1457

Abstract

Criminal acts are often a problem that occurs in Indonesia. Where currently the number of reports handled by the police regarding criminal acts is always there every day. Indonesia's population is increasing and the background of perpetrators who are unemployed is often one of the reasons why the police find it difficult to resolve criminal acts that occur due to limited human resources. To overcome this problem, information is needed that provides areas in Indonesia where criminal acts frequently occur so that the police can make decisions to allocate human resources to protect those jurisdictions from criminal acts that occur. Using data on criminal offenses and the employment of criminal offenders, namely not working from 2021, data was taken from the National Police Criminal Investigation Unit's Pusiknas Annual Journal. The data will be clustered using data mining techniques using the K-Means and K-Medoids algorithms. These 2 algorithms produced 2 clusters with the smallest Davies Bouldin index value found in the K-Means algorithm with a value of 0.272. With the research results which produced 2 clusters, it can be concluded that there are categories of high crime and low crime.
A Graph Theory Approach for Spatial Data-Based Surface Water Flow Modeling Firgiawan, Wawan; Nirwana, Hafsah; Wajidi, Farid; Zainuddin, Zahir; Ahyar, Muh.
SINTECH (Science and Information Technology) Journal Vol. 7 No. 1 (2024): SINTECH Journal Edition April 2024
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v7i1.1480

Abstract

This research proposes an innovative approach that combines graph theory with spatial data to model surface water flow with the Single Flow Direction (SFD) concept, also known as the D8 algorithm. The objective is to show the water flow from the ground surface to a lower place. The research methodology involves collecting spatial data from the Digital Elevation Model (DEMNAS) in raster data type format. Test results show that the effectiveness of the graph approach in modeling water flow can produce clear flow output. This happens because each pixel traversed by water is connected by a line that forms a well-defined water flow path. This study significantly stimulates the development of more sophisticated modeling tools and practical applications in the future. This can help in more efficient management of water resources or produce more accurate flow modeling, contributing to improved understanding and better management of the environment.
Evaluasi Aplikasi Raileo Melalui Analisis Sentimen Ulasan Playstore Dengan Metode Naive Bayes Junianto, Haris; Arsi, Primandani; Kusuma, Bagus Adhi; Saputra, Dhanar Intan Surya
SINTECH (Science and Information Technology) Journal Vol. 7 No. 1 (2024): SINTECH Journal Edition April 2024
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v7i1.1505

Abstract

Abstrak The Raileo application is a staffing platform owned by PT. KAI, functions as a personnel data management system. Effective application development requires data as a basis, and one source of data that can be utilized is user reviews. User reviews provide valuable information regarding application performance, user needs, and security aspects. However, challenges arise in managing review data which often contains sarcasm, creating ambiguous meaning and lowering accuracy levels. This research proposes a solution by applying sentiment analysis using Naive Bayes logarithms to 1047 Raileo review data. This method produces an accuracy rate of 94%, with positive and negative sentiment classification. The research results show the words that appear most frequently in Raileo reviews, such as "eror", "sulit", "titik presensi", "titik absen", "titik lokasi", "bug", "lemot," "gagal", "mantap", "bagus", "oke", "mudah", "mempermudah", "mantul", "lengkap","keren","ok", "inovatif", "inovasi", "semoga", "sukses", dan "membantu". These words can be used as a key to analyze all the sentiments contained in the review. In addition, this research identifies "presence point" as the highest negative sentiment word that needs attention in further development. From this sentiment analysis research, the Raileo application produces the highest sentiment value, namely positive sentiment
Perbandingan Metode K-Medoids dan Metode K-Means Dalam Analisis Segmentasi Pelanggan Mall Rohman, Nur; Wibowo, Arief
SINTECH (Science and Information Technology) Journal Vol. 7 No. 1 (2024): SINTECH Journal Edition April 2024
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v7i1.1507

Abstract

Memahami pelanggan sangat penting untuk mengelola operasi perusahaan. Dengan mengetahui dan memahami setiap pelanggan, dapat meningkatkan komunikasi layanan produk dengan menyesuaikan kebutuhan dan layanan kepada setiap pelanggan. Namun, analisis pelanggan sangat luas sehingga sulit untuk memahami kebutuhan masing-masing pelanggan. Hal ini dapat mencakup berbagai karakteristik dan perilaku pelanggan. Oleh karena itu, diperlukan segmentasi pelanggan untuk mengelompokkan pelanggan berdasarkan perilaku dan karakteristiknya. Tujuan dari penelitian ini adalah membandingkan metode clustering untuk mendapatkan metode yang lebih baik dan optimal dalam mengelompokkan cluster untuk segmentasi pelanggan. Dari permasalahan tersebut, peneliti menerapkan metode CRISP-DM dengan focus pada analisis cluster atau pengelompokkan dengan membandingkan algoritma K-Means dan K-Medoids terhadap Analisa segmentasi pelanggan pada mall. Pada penerapan perbandingan metode K-Means dan K-Medoids, digunakan metode elbow untuk menentukan jumlah cluster yang optimal. Hasil dari metode elbow menunjukkan bahwa penggunaan lima cluster untuk metode K-Means dan empat cluster untuk metode K-Medoids merupakan pilihan yang tepat dalam kasus ini. Langkah selanjutnya adalah mencari nilai Silhouette Coefficient setiap metode yang digunakan dalam perbandingan untuk menentukan metode clustering yang lebih optimal.  Hasil nilai yang diperoleh dari metode Silhouette Coefficient masing-masing metode adalah k-means adalah 0,553 dan k-medoid adalah 0,485, sehingga algoritma pengelompokan segmentasi pelanggan terbaik pada penelitian ini adalah algoritma K-means karena memiliki nilai koefisien siluet maksimum.
Detecting Emotions of Indonesian Songs Based on Plutchik’s Theory using Data Mining Wardani, Deyana Kusuma; Wazaumi, Dwi Diana; Winahyu, Raden Rara Kartika Kusuma
SINTECH (Science and Information Technology) Journal Vol. 7 No. 1 (2024): SINTECH Journal Edition April 2024
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v7i1.1509

Abstract

Listening to songs is a daily activity that everyone engages in. Most people choose songs based on their mood, so a system is needed to detect emotions from song lyrics. Previous research only focused on five basic emotions: happy, sad, love, anger, and fear. In this study, we propose a new method to detect emotions from song lyrics using Plutchik's emotion theory. The data used for this research consisted of 250 song lyrics from Indonesian songs. This research categorizes human emotions into eight: joy, trust, surprise, sadness, disgust, anger, and anticipation. Next, the threshold value is calculated. This value is used to determine the dominant emotion. If the frequency value of an emotion is higher than the threshold value, the system considers it as the dominant emotion. The dominant emotions are then classified into positive and negative emotions using cosine similarity calculations. The sampling technique involves using 30% of the test data, resulting in an accuracy of 0.81.
Optimasi Metode Single Exponential Smoothing Dengan Grid Search Pada Prediksi Nilai Ekspor Migas Medyanti, Wikke Alvina; Faisal, M; Nurhayati, Hani
SINTECH (Science and Information Technology) Journal Vol. 7 No. 1 (2024): SINTECH Journal Edition April 2024
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v7i1.1526

Abstract

Industri migas memiliki peran sentral dalam ekonomi Indonesia, dan kebijakan ekspor yang bijak sangat penting untuk menjaga stabilitas harga dan menghindari dampak inflasi. Data nilai ekspor yang disajikan berupa nilai ekspor minyak mentah dan gas alam di Indonesia. Melalui analisis data ekspor migas dari Januari 1993 hingga Agustus 2023, penelitian ini menemukan bahwa penerapan Grid Search pada SES dengan mengkombinasikan pencarian parameter optimal nilai alpha dan tahun awal meningkatkan akurasi prediksi dengan mengidentifikasi parameter optimal, yaitu alpha sebesar 0,50 dan tahun awal 2023. Hasil pengujian menunjukkan bahwa hasil prediksi metode SES dengan optimasi Grid Search lebih akurat dengan Mean Absolute Percentage Error (MAPE) sebesar 5,783%, lebih rendah dibandingkan dengan metode SES tanpa optimasi (10,172%). Implementasi Grid Search pada SES dapat menghasilkan prediksi yang dapat dan mengurangi dampak ketidakpastian dalam perencanaan ekonomi pada sektor migas.  

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