Article Per Year (5 Year)
p-Index From 2021 - 2026
2.233
P-Index
This Author published in this journals
All Journal DINAMIKA DOTCOM TEKNOLOGI INFORMASI Jurnal Teknologi Informasi dan Ilmu Komputer SMATIKA Syntax Literate: Jurnal Ilmiah Indonesia Wacana: Jurnal Ilmiah Ilmu Komunikasi Journal of Law, Poliitic and Humanities International Journal of Science and Environment Transaction on Informatics and Data Science Mahardika Adiwidia Communica : Journal of Communication Jurnal ISIP: Jurnal Ilmu Sosial dan Ilmu Politik Smatika Jurnal : STIKI Informatika Jurnal
Mochamad Husni
STMIK PPKIA Pradnya Paramita Malang
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Social Sciences Other

Published : 21 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 21 Documents
Search

 1   2   3 
Sharing Knowledge Online Lintas Fungsi sebagai Strategi Komunikasi Organisasi saat Pandemi Husni, Mochamad
Jurnal Mahardika Adiwidia Vol. 1 No. 2 (2022): Mahardika Adiwidia 2022
Publisher : Magister of Communication Science, Sahid University Jakarta.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36441/mahardikaadiwidi.v1i2.744

Abstract

This research is based on the phenomenon that the management of PT Astra Agro Lestari Tbk applies the concept of a matrix organization in terms of the roles and responsibilities of public relations. The matrix pattern makes coordination happen across functions. The implementation of communication programs in the form of media relations in all subsidiaries spread across nine provinces becomes increasingly difficult when the work from home work pattern is implemented. This study wants to find out how the online sharing session strategy is carried out by the Communications and Public Affairs Division in overcoming the lack of knowledge of the Community Development Division as the field implementer of the communication program. The research method used is descriptive qualitative. Collecting data in this study using observation and interview techniques. Research concludes that online coordination can be a solution, but its effectiveness needs to be combined with cultural elements and formal strengths.
Konstruksi Realitas Petani Kelapa Sawit Dalam Film Naga Naga Naga: Analisis Semiotika Charles S. Peirce Husni, Mochamad; Putranto, Algooth
Jurnal Mahardika Adiwidia Vol. 2 No. 1 (2022): Mahardika Adiwidia 2022
Publisher : Magister of Communication Science, Sahid University Jakarta.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36441/mahardikaadiwidi.v2i1.1021

Abstract

The national palm oil industry is increasingly becoming the government's attention. In the midst of opinions about the dominance of big companies and the weak image of smallholder oil palm plantations, demonstrations triggered by the scarcity of cooking oil show the figure of Indonesian oil palm farmers. This study wants to explore how the media constructs the reality of oil palm farmers. The theory used is the social constructions of reality Peter L. Berger and Thomas Luckmann. While the descriptive qualitative research approach uses the semiotic analysis method of Charles Sanders Peirce. The data analysis technique used non-participant observation and secondary documents. The results of the research on the Naga Naga Naga film with the main character Nagabonar concluded that there is a semion with an interpretation of oil palm farmers. With the triangle of meaning method, oil palm farmers are constructed as people who are economically prosperous, have high nationalism, and have intelligent thoughts even though they are not formally educated. This study is also a reflection on the reality that occurs in the community.
Smartwatches, AI, and the Three Faces of Technological Determinism: A Marxist Critique of Digital Capitalism Uli Patrissia, Ressa; Husni, Mochamad
International Journal of Science and Environment (IJSE) Vol. 5 No. 4 (2025): November 2025
Publisher : CV. Inara in Colaboration with www.stie-sampit.ac.id

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijse.v5i3.178

Abstract

This study explores the role of artificial intelligence (AI) in smartwatches through the critical lens of Karl Marx’s theory of technological determinism. In contrast to functionalist and adoption-centered analyses, the research problematizes the ideological and socio-economic structures underpinning wearable technologies. The objective is to reinterpret smartwatches not as neutral innovations but as artifacts embedded in digital capitalism—functioning simultaneously as autonomous agents, social constraints, and political instruments. Employing a qualitative, conceptual methodology grounded in Marxist historiography and critical media studies, the research synthesizes canonical texts with contemporary scholarship on AI, surveillance, and labor. The results reveal that AI-powered smartwatches reinforce capitalist imperatives of productivity, data commodification, and self-discipline, ultimately contributing to behavioral governance and user alienation. This study offers a dialectical framework that exposes the myth of technological neutrality and reframes wearable AI as a site of ideological reproduction. It contributes to communication and technology studies by advancing a historically grounded critique of how digital devices shape, and are shaped by, class dynamics, capitalist rationality, and power structures.
Framing the Floods: Temporal Shifts and Ecological Silences in Indonesian Media Husni, Mochamad
Communica : Journal of Communication Vol. 3 No. 3 (2025): July 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/communica.v3i3.782

Abstract

Indonesia's increasing flood frequency and severity underscore the urgent need for comprehensive disaster reporting that highlights both natural and anthropogenic causes. This study investigates temporal changes in the media framing of flood disasters between 2020 and 2025, with a focus on ecological narratives in Indonesian online news outlets. Using Entman’s four-frame model problem definition, causal attribution, moral evaluation, and remedy recommendation this research analyzed 60 articles from five national media platforms. The articles, covering events in Jakarta, Kalimantan, Jabodetabek, and Pekalongan, were examined using NVivo-assisted coding and critical discourse analysis supported by ecolinguistic perspectives. Results show a persistent reliance on natural disaster framing in both 2020 and 2025, with technical and infrastructural narratives increasingly dominant in recent years. While there is a gradual integration of scientific and policy-relevant vocabulary such as “climate adaptation” and “urban resilience” ecological degradation remains underreported. Comparisons between years reveal an incremental shift toward accountability framing, yet ecological drivers like deforestation or land-use mismanagement continue to receive minimal attention. Furthermore, reliance on official sources and episodic coverage patterns limit public engagement with systemic environmental issues. The discussion highlights structural constraints including corporate influence, low media literacy, regulatory barriers, and editorial dependence on government narratives. Lessons from Global South countries illustrate how integrating grassroots voices and improving journalist training can strengthen environmental reporting. The study concludes by advocating for institutional reforms, enhanced ecological literacy, and the empowerment of community journalism as steps toward more transformative media practices.
Comparison of the Accuracy Between Naive Bayes Classifier and Support Vector Machine Algorithms for Sentiment Analysis in Mobile JKN Application Reviews Septiani, Erni; Akhriza, Tubagus M.; Husni, Mochamad
Transactions on Informatics and Data Science Vol. 1 No. 1 (2024)
Publisher : Department of Informatics, Faculty of Da'wah, UIN Saizu Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v1i1.12232

Abstract

The Mobile JKN (National Health Insurance) application is a form of BPJS Health's commitment to implementing health insurance programs since 2014. The large number of reviews of the Mobile JKN application on the Google Play Store requires sentiment analysis with an algorithm that produces the best accuracy. This research compares the accuracy obtained from the Naive Bayes Classifier (NBC) and Support Vector Machine (SVM) algorithms. This algorithm is implemented directly in sentiment analysis and combined with the Synthetic Minority Over-Sampling Technique (SMOTE) technique to overcome data imbalance. The data in this research was obtained from reviews of the Mobile JKN application on the Google Play Store using the data scraping method. We use data scraping and labeling processes before performing sentiment analysis. The sentiment analysis process includes text preprocessing and processing (modeling) by dividing the data into 30%, 40%, and 50% test data, with the rest becoming training data. The results of this research showed that the algorithm with the best accuracy was the NBC algorithm using the SMOTE technique with 50% test data and the SVM algorithm without the SMOTE technique with 50% test data. Both give the same accurate results, namely 0.90 or 90%. Experiments show that the amount of test data and the application of SMOTE affect the accuracy of the two compared algorithms.
Demagog Hoaks Kapitalisme Digital Perspektif Theodor W. Adorno dan Budaya Massa Patrissia, Ressa Uli; Husni, Mochamad
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v9i5.15860

Abstract

Kekhawatiran tentang pengaruh kapitalisme terhadap masyarakat, seperti yang diungkapkan oleh Adorno dan Horkheimer dalam Dialectic of Enlightenment. Tujuannya untuk memahami bagaimana industri budaya dan digitalisasi memengaruhi budaya dan politik kontemporer. Metode penelitian melibatkan analisis literatur dan data dari berbagai sumber untuk menggambarkan dampak digitalisasi, khususnya dalam penyebaran berita palsu dan pembentukan opini publik. Hasil menunjukkan bahwa teknologi, seperti media sosial dan kecerdasan buatan, telah menjadi alat utama dalam memengaruhi persepsi dan perilaku politik masyarakat. Hal ini tercermin dalam penyebaran berita palsu yang semakin meluas dan penggunaan teknologi untuk memanipulasi opini publik. Kesimpulannya, perlu perhatian serius terhadap masalah ini, dengan memperkuat literasi digital dan kritis masyarakat serta menegakkan regulasi yang membatasi penyebaran berita palsu.
Pengalaman Self-Nudging dalam Relasi Komunikasi Algoritma Manusia dan Smartwatch Patrissia, Ressa Uli; Husni, Mochamad
WACANA: Jurnal Ilmiah Ilmu Komunikasi Volume 24, No. 2 December 2025
Publisher : Universitas Prof. Dr. Moestopo (Beragama)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32509/wacana.v24i2.5783

Abstract

This study examines users’ subjective experiences of algorithmic communication generated by smartwatches in the context of health decision-making. Using a critical phenomenological approach and an Interpretative Phenomenological Analysis (IPA) design, the study involved 12 active smartwatch users from urban areas in Indonesia who had used health-notification features for more than six months. Data were collected through in-depth interviews, digital observation, and reflective journaling. Findings reveal that smartwatch notifications function not merely as reminders but subtly and repeatedly shape users’ behaviors and health decisions, often without deliberate reflection. The concept of self-nudging, ideally grounded in user autonomy, frequently shifts into pseudo-self-nudging and even algorithmic steering, where the algorithm becomes the dominant agent guiding user actions. Users experience ambivalence—feeling supported yet simultaneously controlled—indicating a shift in bodily authority from personal intuition to algorithmic recommendations. These findings highlight the need for algorithmic literacy and ethical design in wearable technologies to ensure that users retain agency and reflective awareness when facing digital interventions that are prescriptive and normative.
Klasifikasi Sentimen Terhadap Badan Penyelenggara Jaminan Sosial (BPJS) Pada Media Sosial Twitter Menggunakan Naive Bayes Mahmud Yunus; Mochamad Husni; Muhammad Miqdad Mufadhdhal
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 11 No 02 (2021): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v11i02.577

Abstract

BPJS Kesehatan is listed as the agency that provides insurance financial services with the largest number of public complaints in the Ombudsman in 2019. The number of complaints on the official BPJS Kesehatan twitter account can be an indication of the level of satisfaction and public sentiment towards BPJS Kesehatan services. Twitter can be used to convey the experiences, ideas, complaints, opinions, or facts presented. The Tweet can be either a positive or a negative opinion. To find out, there needs to be an existing data processing process, so that it can be classified as a positive and a negative opinion. The classification method used in this study is the Naive Bayes Classifier. This study aims to see the tendency of the public towards BPJS Kesehatan based on sentiment classifications and to see the level of accuracy of the Naive Bayes Classifier method in classifying BPJS Kesehatan sentiments on Twitter social media. The data used were 780 tweet data from March to May 2020. The results of model testing using the Confusion Matrix resulted in an accurate performance of 86.25%, precision of 84.92%, recall of 87.78%, and f-measure of 86, 37%. As well as the results of testing the data in May 2020, there were 52% of tweets in the positive sentiment category, and 48% of tweets in the negative category
Smartwatch sebagai Aktor Komunikasi dalam Jejaring Sosioteknologis Patrissia, Ressa Uli; Husni, Mochamad
Jurnal ISIP: Jurnal Ilmu Sosial dan Ilmu Politik Vol. 22 No. 2 (2025): Jurnal ISIP: Jurnal Ilmu Sosial dan Ilmu Politik
Publisher : Institute of Social and Political Science Jakarta (Institut Ilmu Sosial dan Ilmu Politik Jakarta - IISIP Jakarta)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36451/jisip.v22i2.443

Abstract

Smartwatch telah berkembang dari perangkat pendukung menjadi aktor komunikatif yang membentuk ritme, kebiasaan, dan makna sosial dalam kehidupan sehari-hari pengguna. Penelitian ini bertujuan menganalisis peran smartwatch sebagai aktor nonmanusia dalam jejaring komunikasi serta menjelaskan bagaimana makna dikonstruksi oleh kelompok sosial yang berbeda. Penelitian ini menggunakan pendekatan kualitatif dengan etnografi digital terhadap 12 informan lintas usia dan profesi. Analisis dilakukan secara induktif melalui empat kategori tematik, yaitu ritme, habit, makna, dan intervensi. Hasil penelitian menunjukkan bahwa smartwatch memediasi ritme komunikasi, membangun kebiasaan melalui skrip algoritmik, serta menciptakan relasi afektif dan simbolik yang berbeda antar kelompok pengguna. Perspektif Actor–Network Theory (ANT) menjelaskan bagaimana smartwatch memengaruhi tindakan melalui mediasi temporal dan intervensi, sedangkan Social Construction of Technology (SCOT) menunjukkan variasi makna yang dinegosiasikan sebagai efisiensi, keamanan, performa, atau gaya hidup. Temuan ini menegaskan bahwa komunikasi manusia–teknologi bersifat ko-konstitutif. Teknologi dan pengguna membentuk satu sama lain dalam jaringan sosial-teknologis. Penelitian selanjutnya disarankan mengeksplorasi komparasi lintas budaya dan konsekuensi etis dari intervensi algoritmik dalam kehidupan sehari-hari.
Perbandingan Metode K-Nearest Neighbor dan Artificial Neural Network untuk Klasifikasi Indeks Pembangunan Manusia Widayanti, Rahayu; Husni, Mochamad; Maknunah, Jauharul; Widyadhana Putri , Garwita
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 1: Februari 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026131

Abstract

Indeks Pembangunan Manusia (IPM) adalah pengukuran perbandingan standar hidup, harapan hidup, dan pendidikan di semua negara. IPM digunakan sebagai indikator untuk menilai aspek kualitas pembangunan, mengklasifikasikan negara, dan mengukur pengaruh kebijakan ekonomi terhadap kualitas hidup. IPM adalah data strategis karena selain digunakan sebagai ukuran kinerja pemerintah, juga digunakan sebagai alokator penentuan Dana Alokasi Umum (DAU). Pengukuran Indeks pembangunan manusia sangat penting bagi pemerintah, karena digunakan sebagai pendukung keputusan perencanaan pembangunan manusia di suatu wilayah. Oleh karena itu pemilihan metode pengukuran IPM yang memiliki akurasi tinggi sangat penting, agar keputusan perencanaan pembangunan manusia menjadi efektif dan tepat sasaran. Tujuan penelitian ini adalah membandingkan akurasi dari metode K-Nearest Neighbor dan Artificial Neural Network untuk klasifikasi IPM menggunakan data kabupaten dan kota di Pulau Jawa. Hasil penelitian menunjukkan bahwa metode K-Nearest Neighbor menggunakan 80%-20% data training dan testing, pada nilai K=7 menunjukkan tingkat akurasi sebesar 95,83%, sedangkan pada metode Artificial Neural Network dengan pembagian data 70%-30% menghasilkan tingkat akurasi sebesar 94,44%. Berdasarkan perbandingan tersebut Metode K-Nearest Neighbor mempunyai akurasi yang lebih baik dibandingkan metode Artificial Neural Network. Namun evaluasi menggunakan  Fold Cross Validation, dengan nilai K=3, pada metode K-Nearest Neighbor menunjukkan akurasi terbaik sebesar 84,85%, sedangkan pada metode Artificial Neural Network terdapat overfitting sehingga hasil kurang baik. Penerapan metode KNN dan ANN pada klasifikasi IPM kabupaten/kota di Pulau Jawa menunjukkan bahwa antara kedua metode memiliki kelemahan, dimana pada pembagian data dengan nilai akurasi tertinggi, bukan merupakan model terbaik. Pada kedua metode dengan tingkat akurasi yang tertinggi, berdasarkan Fold Cross Validation bukan merupakan model terbaik, sehingga dapat disimpulkan bahwa kedua metode tersebut tidak lebih baik dari yang lain.   Abstract The Human Development Index (HDI) is a comparative measure of living standards, life expectancy, and education across countries. The HDI is used as an indicator to assess aspects of development quality, classify countries, and measure the impact of economic policies on quality of life. The HDI is strategic data because in addition to being used as a measure of government performance, it is also used as an allocator for determining the General Allocation Fund (DAU). Measuring the Human Development Index is very important for the government, because it is used to support decisions on human development planning in a region. Therefore, choosing a high-accuracy HDI measurement method is very important, so that human development planning decisions are effective and on target. The purpose of this study was to compare the accuracy of the K-Nearest Neighbor method and Artificial Neural Network for HDI classification using district and city data in Java. The results showed that the K-Nearest Neighbor method used 80%-20% training and testing data, at a value of K = 7 showed an accuracy level of 95.83%, while the Artificial Neural Network method with a data division of 70%-30% produced an accuracy level of 94.44%. Based on the comparison, the K-Nearest Neighbor method has better accuracy than the Artificial Neural Network method. However, the evaluation using Fold Cross Validation, with a value of K = 3, in the K-Nearest Neighbor method shows the best accuracy of 84.85%, while in the Artificial Neural Network method there is overfitting so that the results are not good. The application of the KNN and ANN methods to the classification of the HDI of districts/cities in Java shows that both methods have weaknesses, such that the model with the highest accuracy in data distribution is not the best model. In both methods with the highest level of accuracy, based on Fold Cross Validation, it is not the best model, so it can be concluded that the two methods are not better than the others.
Co-Authors Achmad Saiful Rizal Akhriza, Tubagus M. Andre Kurniawan Basuki, Pritha Ayodya Dwi Safiroh Utsalina Jauharul Maknunah Lestari, Elvira Verdhi Mahmud Yunus Muhammad Miqdad Mufadhdhal Putranto, Algooth Rahayu Widayanti Ressa Uli Patrissia Sa’atul Ihsan Saipi -, Saipi Septiani, Erni Widyadhana Putri , Garwita