Surya Sumpeno

Sepuluh Nopember Institute of Technology

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Mauridhi Hery Purnomo

Sepuluh Nopember Institute of Technology

Mochamad Hariadi

Sepuluh Nopember Institute of Technology

Diana Purwitasari

Sepuluh Nopember Institute of Technology

Adhi Dharma Wibawa

Sepuluh Nopember Institute of Technology

Adri Gabriel Sooai

Universitas Katolik Widya Mandira

Muhtadin Muhtadin

Sepuluh Nopember Institute of Technology

Joan Santoso

Institut Sains dan Teknologi Terpadu Surabaya

Ulla Delfana Rosiani

State University of Malang

Chastine Fatichah

Sepuluh Nopember Institute of Technology

Eko Mulyanto Yuniarno

Sepuluh Nopember Institute of Technology

Cooperative Institutions

Sepuluh Nopember Institute of Technology

107

Institut Sains dan Teknologi Terpadu Surabaya

Institut Teknologi Adhi Tama Surabaya

Universitas Dian Nuswantoro

State University of Malang

Trunojoyo University

Universitas Katolik Widya Mandira

Universitas Negeri Surabaya

University of Indonesia

Universitas Gadjah Mada

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Fuzzy Multi-Attribute Decision Making untuk Klasifikasi Potensi Kewirausahaan Berdasarkan Theory of Planned Behavior (Fuzzy Multi-Attribute Decision Making for Classifying Entrepreneurial Potential based on Theory of Planned Behavior)

Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI) (2020)

Nova Rijati Diana Purwitasari Surya Sumpeno Mauridhi Hery Purnomo

Indonesia government has launched a program to encourage youth entrepreneurship as a strategy to improve national economy. This paper proposes a method to find an entrepreneurial potential based on academic behavior features that are extracted from the Higher Education Database PDDikti. The proposed approach applies the Fuzzy Multi-Attribute Decision Making (FMADM) technique. Rules for extracting features of student academic behavior were following Theory of Planned Behavior (TPB) and resulting in 14 features. The FMADM model combines Fuzzy Simple Additive Weighting and Fuzzy Technique for Order Preference by Similarity to Ideal Solution, which is called FSAW-TOPSIS. Friedman Test demonstrated that FSAW-TOPSIS gives more optimal solution with the highest Mean Rank of the potential entrepreneurial value of 2.96. Besides, through Hamming Distance Test, FSAW-TOPSIS results the best order with a 98% percentage and ranking of the smallest Squared Error of 0.3%, which makes the proposed model offered a better solution. It can be concluded that using TPB variables in PDDikti environment with FSAW-TOPSIS technique provides an optimal recommendation on student entrepreneurship potential, which can be used as a part of a decision-making system for higher education management.

TOPSIS

Ideal solution

Similarity (geometry)

10.22146/jnteti.v9i1.118

Cite

Citations (3)

An Immersive Approach for Scholar Profile Visualization

Diana Purwitasari Fariz Ardin Adhiyasa Nanik Suciati Hadziq Fabroyir Surya Sumpeno

Interactive visualizations can help users to find researchers and their research profiles contributing to the challenge of information overload due to the increasing number of published articles. Previous studies using bibliographical data on scholars and their articles have focused primarily on 2D visualizations, especially those displaying topics of researcher expertise. Alternatively, our work contributed to a 3D visualization framework for scholars rather than articles based on published research using Virtual Reality (VR) to create an immersive user experience. Procedures in the framework include collecting Scopus article metadata to generate subject domains, mapping articles-domains to identify scholars' research expertise, transforming scholar data points, and building the visualization inside the virtual environment. The creation of subject domains and the labeling process of Scholar metadata utilizes Scopus metadata originating from Scopus subject area. After labeling the article metadata, the transformation process that employed both metadata of Scopus and Scholar led to researcher coordinates for a scatter diagram panel in the VR space. Those labels and coordinates become parts of user tasks that yielded positive feedback for the proposed VR-based visualization of bibliographical data. Through some questionnaires, our experiments demonstrated a proof of concept according to the proposed framework resulting in good usability, especially concerning clarity, ease of access, and layout settings.

CLARITY

10.1109/civemsa53371.2022.9853688

Cite

Citations (1)

ANALISA LEVEL LUMINANSI CVBS UNTUK TRACKING ANTENNA SYSTEM PADA SET TOP BOX DVB-T2

Prosiding Sains Nasional dan Teknologi (2018)

Herti Miawarni M. Mahaputra Hidayat Surya Sumpeno Eko Setijadi

Tracking antenna system dalam penggunaan set top box DVB-T2 adalah teknologi tepat guna yang dapat memudahkan pengguna dalam mendapatkan kualitas video yang optimal. Parameter input yang handal adalah salah satu syarat dalam realisasi tracking antenna system. Pada penelitian ini mengusulkan level luminansi pada sinyal analog CVBS sebagai parameter utama. Metode analisa rata-rata dan metode analisa garis juga diusulkan untuk menganalisa level luminansi. Hasil uji coba menunjukkan bahwa level luminansi CVBS layak untuk digunakan sebagai parameter input. Hal ini disimpulkan dari perbedaan nilai ADC yang signifikan pada beberapa kondisi visual. Tantangan terbesar penggunaan level luminansi CVBS sebagai parameter input adalah kemunculan OSD (On Screen Display). Hasil uji coba menunjukkan bahwa metode analisa rata-rata dapat terganggu oleh kemunculan OSD. Sementara metode analisa garis tidak terganggu oleh kemunculan OSD. Meskipun demikian, kedua metode memiliki kelebihan dan kekurangan yang nantinya dapat dikombinasikan untuk perbaikan kinerja.Â Kata kunci : output CVBS, DVB-T2, level luminansi, set top box, tracking antenna system.

10.36499/psnst.v1i1.2363

Cite

Citations (0)

AN IMPROVED TECHNIQUE FOR LVQ-BASED VIDEO OBJECT EXTRACTION

Surya Sumpeno

This paper presents a semi-automatic algorithmfor video object extraction, in which a semantic ob-ject of interest is de ned in advance in a key frameprovided by human. For ordinary video frames,the speci ed video object is tracked and segmentedautomatically using Learning Vector Quantization(LVQ). This paper also presents a technique forimproving extraction performance of the proposedalgorithm and reducing computation time. Ex-perimental evaluation using MPEG standard testvideosequencesdemonstratesthat the proposedal-gorithm is able to extract the video object with lowerror.

Learning vector quantization

Source

Cite

Citations (0)

An Information Retrieval Approach to Finding Similar Questions in Question-Answering of Indonesian Government e-Procurement Services using TF*IDF and LSI Model

Syamsul Bahri Surya Sumpeno Supeno Mardi Susiki Nugroho

In the implementation of e-procurement application in the government of the Republic of Indonesia, one of the important issues is how to find relevant questions in the archive of a question answering (QA) service with the question asked by the user. A common method used for finding relevant documents is representing text documents into vector space model (VSM). Relevance between query and documents can be calculated using document similarities theory, by comparing the deviation of angles between each document vector and the query (from user question) vector where the query is represented as the same kind of vector as the documents. The Vector Space model algorithms widely used are Term Frequency * Inverse Document Frequency (TF*IDF) and Latent Semantic Indexing (LSI), however both models have their respective limitation. Considering that problem, this paper proposed hybrid model that combines TF*IDF and LSI to fix some limitations on both. From the experimental results, it is found that the proposed model is outperform (P@1=0.67) compared to TF*IDF model (P@1=0.27) and LSI model (P@1=0.4) that stand alone.

tf–idf

Vector space model

Relevance

10.1109/iciteed.2018.8534856

Cite

Citations (3)

Conflict of Interest based Features for Expert Classification in Bibliographic Network

Diana Purwitasari Akhmad Bakhrul Ilmi Chastine Fatichah Willy Achmat Fauzi Surya Sumpeno

Countless approaches of feature extraction in the expert classification problem employ text contents and network structures from bibliographic metadata of published articles. The content part often use title and abstract while the structure part utilize co-authorship and citation. On citation data, the classifier method works on a feature of citation quantity since a frequently cited author is presumed to have more expertise. Citation misconduct occurs if there is no subject relation between citing and cited articles. Therefore, the misconduct becomes a challenge for evaluation of citation quality. Here, the problem is to classify experts with features that can indicate citation misconduct. To address this problem, our contribution exploited the quality and the quantity of citations in feature extraction designed for classifying experts. Co-authorship that influence the misconducts is called as Conflict of Interest (CoI) situation. Accordingly, the class labels are experts with or without CoI indication. We proposed three ratio features of (1) self-citation to represent the citation quantity, then (2) subject similarity of author interests and article contents, as well as (3) subject similarity of citing and cited articles to determine the citation quality. There are various word phrases used in subjects with similar contexts. Therefore the proposed CoI-based features for the citation quality took on deep learning approaches for understanding natural language. Our experiments exercised a selection of data from one of the common datasets in bibliographic related problems called as AMiner. We selected ± 15K articles from the original data of ± 2M articles in the experiments. The results showed that our proposed features classified experts with CoI indication by accuracy value of ± 60%. Although the first feature of citation quantity was not significant for categorizing experts, other features of citation quality confirmed more profound evidence.

Citation analysis

Feature (linguistics)

10.1109/cenim.2018.8710931

Cite

Citations (4)

PENYUSUNAN DATABASE EKSPRESI WAJAH PEREMPUAN INDONESIA-JAWA BERBASIS PENANDA 3D UNTUK PENGENALAN EMOSI

Surya Sumpeno Muhtadin Muhtadin

Ekspresi wajah merupakan topik yang sering digunakan untuk pengembangan beberapa bidang penelitian, seperti bidang keamanan, hiburan, dan psikologi. Penelitian yang melibatkan ekspresi wajah selalu menuntut penggunaan kualitas sumber data yang baik. Pada penelitian ini dilakukan pembuatan sumber data ekspresi wajah yang berbentuk database ekspresi wajah perempuan Indonesia-Jawa berbasis Penanda 3D atau yang disebut sebagai JIFFE-3D Database (Javanese-Indonesian Female Facial Expression 3D Database). JIFFE-3D Database berisi ekspresi wajah yang mencerminkan emosi dasar perempuan Indonesia, seperti senang, jijik, takut, marah, terkejut, dan sedih. Pembuatan JIFFE-3D Database dilakukan menggunakan sistem motion capture untuk menangkap perubahan ekspresi wajah yang ditandai dengan pergeseran penanda pada bagian-bagian wajah. Ekspresi wajah tersebut direkam menggunakan multi camera dan penanda yang disimpan dalam bentuk data 3D. Data JIFFE-3D Database memiliki kualitas data yang baik dengan ketentuan bahwa data yang digunakan hanya menggunakan penanda yang berpengaruh terhadap perubahan ekspresi wajah. Pada proses pengklasifikasian emosi, data JIFFE-3D Database dapat menghasilkan nilai akurasi yang cukup tinggi dengan menggunakan metode klasifikasi kNN, yaitu sebesar 90.71% untuk data .bvh dan sebesar 86.67% untuk data .trc.

Source

Cite

Citations (0)

Search Engine Optimization Menggunakan Particle Swarm Optimization

Sudarmaji Surya Sumpeno Mochamad Hariadi

Search engine optimization

Source

Cite

Citations (0)

PENYUSUNAN DATABASE EKSPRESI WAJAH PEREMPUAN INDONESIA-JAWA BERBASIS PENANDA 3D UNTUK PENGENALAN EMOSI

Surya Sumpeno Muhtadin Muhtadin

Source

Cite

Citations (0)

Ekstraksi Ciri Produktivitas Dinamis untuk Prediksi Topik Pakar dengan Model Discrete Choice

Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI) (2018)

Diana Purwitasari Chastine Fatichah Surya Sumpeno Mauridhi Hery Purnomo

Rekomendasi pakar yang aktif atau produktif dapat mendukung kelancaran kolaborasi penelitian. Kegiatan publikasi dan sitasi menjadi indikasi produktivitas seorang pakar. Konfirmasi ketertarikan pakar akan suatu subjek terlihat dengan produktivitasnya di topik tersebut. Dikarenakan fokus riset pakar dapat berubah, kontribusi pada makalah ini adalah model Discrete Choice ( Discrete Choice Model , DCM) untuk prediksi topik pakar yang dianggap utama berdasarkan produktivitasnya. DCM menggunakan ciri hasil ekstraksi data bibliografi, yaitu relasi sitasi dan teks judul serta intisari dari artikel milik pakar. Sebelum proses ekstraksi ciri produktivitas dan ciri dinamis untuk representasi perubahan fokus riset dari tiap pakar, pengklasteran teks judul dengan KMeans ++ dilakukan untuk identifikasi topik riset. Ada enam jenis ciri produktivitas dan lima kriteria dinamis pada tiap ciri untuk merepresentasikan perilaku pakar terhadap fokus risetnya, sehingga untuk setiap topik hasil pengklasteran akan direpresentasikan sebagai suatu pilihan bidang riset dari seorang pakar dengan 30 ciri hasil ekstraksi. Uji coba yang dilakukan menggunakan pendekatan regresi logistik multinomial untuk DCM dan log-likelihood sebagai indikator pengujian model dari ciri. DCM yang dihasilkan menunjukkan perilaku pakar terkait produktivitas dengan aktif publikasi dan banyak menerima sitasi memberikan nilai presisi pada proses prediksi topik sebesar 80%. Beberapa kombinasi ciri memberikan hasil prediksi yang lebih baik. Hal tersebut terlihat dengan nilai presisi yang lebih rendah, yaitu 60%, jika prediksi topik dilakukan dengan model dari ciri terkait banyak publikasi saja atau banyak sitasi saja.

10.22146/jnteti.v7i4.460

Cite

Citations (2)