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All Journal Lumen Veritatis: Jurnal Filsafat dan Teologi INJECT Interdisciplinary Journal of Communication Journal of Intelligent Systems Technology and Informatics
Iqbal Hussain Alamyar
UIN Prof. K.H. Saifuddin Zuhri Purwokerto
Religion Humanities Computer Science & IT Control & Systems Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Social Sciences

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From Husserl to Merleau-Ponty: Tracing the Arc of Phenomenology Iqbal Hussain Alamyar
Lumen Veritatis: Jurnal Filsafat dan Teologi Vol 15 No 1 (2024): LUMEN VERITATIS: Jurnal Filsafat dan Teologi| APRIL 2024
Publisher : Program Studi Filsafat Universitas Katolik Widya Mandira Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30822/lumenveritatis.v15i1.3016

Abstract

This research traces the key developments in phenomenological thought from its inception with Edmund Husserl to its evolution under Maurice Merleau-Ponty. The primary objective is to understand the continuity and divergence in their perspectives, and the influence on phenomenology’s progression as a philosophical tradition. The study employs a conceptual and thematic analysis method to elucidate these developments by examining their foundational texts. The findings reveal a nuanced understanding of this evolution, highlighting both Husserl’s establishment of transcendental phenomenology focused on pure consciousness and Merleau-Ponty’s transformative turn toward embodied experience. By synthesizing connections between their projects, this analysis provides a comprehensive view of the arc of phenomenological thought. Keywords: Phenomenology, Husserl & Merleau-Ponty, Intentionality, Consciousness, Embodiment
The Semiotics of Snapchat Filters: Understanding Their Influence on User Self-Presentation Iqbal Hussain Alamyar; Javid Hussain
INJECT (Interdisciplinary Journal of Communication) Vol. 9 No. 1 (2024)
Publisher : FAKULTAS DAKWAH UIN SALATIGA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18326/inject.v9i1.1848

Abstract

This research explores the semiotics of Snapchat filters and their influence on user self-presentation. Utilizing the Media Culturalist Perspective and Saussurean Visual Semiotic Theory, it investigates how Snapchat filters, as visual signs, construct meaning and shape user self-presentation on the platform. The study employs a qualitative research design, collecting data through semi-structured interviews with Snapchat users and performing a visual semiotic analysis of the filters. Thematic analysis identifies patterns within the data, providing insights into the cultural implications of Snapchat filters. This research fills a significant gap in existing literature by focusing on the cultural impact of these filters, integral to the Snapchat experience. The findings highlight the role of visual elements in social media self-presentation, contributing to broader discussions on digital culture. These qualitative insights reveal how Snapchat filters shape online self-presentation, offering implications for users, the platform, and future research.
Empirical Benchmarking of Hybrid Retrieval in Educational Conversational AI: Accuracy‑Latency Trade‑offs and Robustness Abdul Saboor Hamedi; Iqbal Hussain Alamyar; A.A. Waskita
Journal of Intelligent Systems Technology and Informatics Vol 2 No 2 (2026): JISTICS, July 2026
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v2i2.245

Abstract

This study investigates the comparative performance of lexical, semantic, and hybrid retrieval strategies in educational conversational AI, with a focus on accuracy-latency trade‑offs and robustness across diverse query types. A controlled experimental framework was implemented using PostgreSQL’s ts_rank for lexical retrieval, pgvector embeddings for semantic retrieval, and two fusion strategies: Linear Weighted Fusion and Reciprocal Rank Fusion. The evaluation corpus consisted of approximately 50,000 text chunks extracted from 2025 arXiv AI/ML papers, and a benchmark of 100 queries spanning conceptual, factual, procedural, comparative, and miscellaneous categories was executed. Effectiveness was measured using NDCG@10, Precision@5, and MRR, while efficiency was quantified via end‑to‑end latency. Relevance judgments were generated through an AI‑as‑a‑Judge pipeline to ensure scalability and reproducibility. Results showed that semantic and hybrid methods achieved a high accuracy mean NDCG@10 ≈ 0.91 but incurred latency costs between 227-505 ms. Lexical retrieval was fastest, 88 ms, but substantially less accurate, 0.346. Hybrid‑Linear fusion emerged as the most robust strategy, winning 66% of queries in the Winner‑Take‑All analysis, while semantic search excelled in conceptual queries and lexical search in acronym‑based factual lookups. Reciprocal Rank Fusion achieved comparable mean accuracy but failed to dominate in any category. The findings highlight a clear quality–speed dichotomy and establish Hybrid‑Linear fusion as the most dependable retrieval method for educational chatbots. For latency‑sensitive applications, semantic search offers the best balance of responsiveness and accuracy. The study provides actionable design guidelines and identifies future directions, including corpus generalization, human evaluation calibration, intelligent query routing, and latency optimization.
Co-Authors A.A. Waskita Abdul Saboor Hamedi Javid Hussain